Described invention and its embodiments, in part, facilitate discovery of ‘Most Recent Common Ancestors’ in the family trees between a massive plurality of individuals who have been predicted to be related according to amount of deoxyribonucleic acids (DNA) shared as determined from a plurality of 3rd party genome sequencing and matching systems. This facilitation is enabled through a holistic set of distributed software Agents running, in part, a plurality of cooperating Machine Learning systems, such as smart evolutionary algorithms, custom classification algorithms, cluster analysis and geo-temporal proximity analysis, which in part, enable and rely on a system of Knowledge Management applied to manually input and data-mined evidences and hierarchical clusters, quality metrics, fuzzy logic constraints and Bayesian network inspired inference sharing spanning across and between all data available on personal family trees or system created virtual trees, and employing all available data regarding the genome-matching results of Users associated to those trees, and all available historical data influencing the subjects in the trees, which are represented in a form of Competitive Learning network. Derivative results of this system include, in part, automated clustering and association of phenotypes to genotypes, automated recreation of ancestor partial genomes from accumulated DNA from triangulations and the traits correlated to that DNA, and a system of cognitive computing based on distributed neural networks with mobile Agents mediating activation according to connection weights.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented system (100), comprising a holistic set of computerized sub-systems and methods (100-5000), each illustrated in corresponding FIGS. 1-50), which act collectively to enrich a plurality of shared databases, and generate a plurality of reports and graphical displays, and which collectively cooperate to improve and expand individual genealogic family trees, and shared common family trees. 2. The system of claim 1, wherein said system addresses a plurality of problems with a plurality of solutions comprising: a. address the problem that much or most data in User's family trees may not be qualified in terms of its' accuracy and the User's confidence in it, in a structure and format that is easily used by a computer automated system, and that this system (100) addresses this problem by introducing a Knowledge Management system of meta-data to record these confidences, and a means for Users to specifically enter subjective confidence metrics, and a system of Agents to repeatedly check the accuracy of data and to record it, and; b. address the problem that the data and knowledge of ‘who DNA matched to any particular User and over what segment(s)’ is not generally available except to the particular User, including that: i) the full list of DNA matches (putative DNA Cousins) of Users are not shared and that this information, if available to a holistic system such as system (100), can potentially leverage all the information available, and that this is not the case with the known current art, including the ‘Family Networks’ or so-called ‘DNA Circles’ which are limited in depth (generations back in time) and which may associate a User to erroneous DNA Circles when the User DNA-matches several members of a ‘DNA Circle’, and those member's DNA match each other to a certain degree and actually do share an MRCA, but that MRCA is not the actual MRCA between the User and the DNA match members of the DNA Circle, which is often due to cases of endogamy, and that this system (100) avoids these errors through the holistic aggregation of information into a Competitive Neural Network (CNN), and in part through exclusions of false MRCA's by DNA Agents (932) tracing and mapping the DNA segment flows to their origins; ii) the shared DNA match segment data are not shared between the various DNA assisted Ancestry services, and said system (100) provides data structures and input mechanisms to allow Users to efficiently and securely share this information with the said system (100) such that the various sub-systems may operate on the data; iii) the discovered, or most probable, MRCA found between sets of DNA matched User's are not shared or published in User's trees or in a common family tree, and that system (100) and its' sub-systems does share this information such that the enhanced confidence derived from the DNA supported MRCA can propagate to other trees which have the Ancestor, and: c. address the problem that the compute requirements of the system grows with the number of Users involved, and that this system (100) mitigates this problem by potentially using the User's personal compute systems and by introducing a distributed Agent computing model which can run on peer-to-peer networks or monolithic or cluster computing systems, and; d. address the problem of encoding the potential relationships and other associations between Ancestors in various family trees, and that this system (100) solves this by introducing the concept of a distributed Competitive Neural Network (CNN) wherein the nodes of the CNN are comprised of Virtual Individual Ancestors, Virtual Attribute Nodes containing attributes shared between Ancestors, Virtual DNA nodes to capture the relationship of DNA between Ancestors, and various ‘In Common With’ (ICW) nodes to capture various commonalities including two Users who both match a third User, and common Ancestors found in DNA matched cousin's trees which provide hints that these User's may lead to the MRCA between the two Users, and wherein the connections between the various Nodes are weighted to reflect the confidence and importance of the association between the Nodes, and; e. address the problem that the known available DNA-assisted Ancestry services do not provide a multi-faceted system to check which Ancestors between two family trees of two DNA-matched Users are potentially related, associated by social circles or time and place, or are the same person, employing multiple factors and methods, and that system (100) and it's sub-systems uniquely provide these enhanced capabilities, including: i) discovering and recording commonalities between the Ancestors in compared trees via weighted connection nodes; ii) scaling the impact of the measured commonalities by the confidence in the data in the respective trees; iii) utilizing logical rules which can intelligently utilize information like the proximity of Ancestors in place and time, and can add nodes connecting Ancestors who could have crossed paths during their reproductive years, according to their known addresses; iv) using a system described herein as ‘In Common With Disembodied Cousins’ (ICW-DC) analysis, wherein the common individuals found in the trees of DNA matched Users are annotated with that information, and the pattern of the incidence of ICW-DC' s can be used to focus research to a cluster of common ancestors, and that the form of the cluster in the tree (fan-up or fan-down), can be used to logically infer where shared DNA flowed and thus whether a MRCA is above a cluster or below it; v) a neural network system similar to a convolutional neural network, wherein the various metrics of similarity are measured in different stages of the neural network, with each stage similar to a feature detection, and passing on to the next stage the positive or negative determination of whether a feature or metric passed a threshold, and that this neural network system may be trained on existing family trees; and, f. address the problem of discovering or narrowing the possibilities for the most likely ‘Most Recent Common Ancestor’ (MRCA) between each pair of DNA matched Users, wherein this problem is severely exacerbated by low-confidence data in family trees and a lack of systematic means of determining which Ancestors are the most relevant to finding the MRCA, and that if the various Users' family trees were qualified in terms of the accuracy and confidence in their data as this system facilitates, and if there were ample data in terms of recording which Ancestors in the family trees of two DNA matched Users were similar or likely to have been associated, then various techniques in Artificial Intelligence (AI) and Machine Learning (ML) could more easily be applied to the problem, and that this system (100) does this by using multiple factors including and comprising: i) constraint-driven problem space reduction, wherein, for example, the distance to an MRCA between DNA matched Users' accounts for not just one pair of DNA matched Users and their predicted Genetic Distance, but rather, all available and relevant DNA matched Users; ii) competitive associative network techniques (using the CNN) to give greater attraction to Ancestors in different trees who are similar on multiple factors, and to inhibit, or repel, Ancestors in DNA matched trees who are less likely to be the MRCA; iii) combinatorial optimization by calculating the fitness of an assignment of putative MRCA to ancestors, using several algorithms; iv) logical process of elimination across a plurality of DNA matched Users, wherein the increase of probability that a particular common Ancestor is a particular MRCA between a pair of DNA matched Users, reduces the probability that other Ancestors are the MRCA, and thus increases the probability that those other Ancestors are the MRCA for some other DNA match, unless the other DNA match has sufficient evidence to positively associate them to the noted MRCA; g. address the problem that if an MRCA has been found between a plurality of DNA matched Users, that the shared DNA between those Users may be associated to the discovered MRCA, and that this system (100) automates this process by associating the DNA segment to the MRCA nodes, and: i) if two or more of the segments associated to an MRCA overlap by several centiMorgans, and are thus matching in the overlap, then the two or more segments may be combined into a larger segment, and that this larger segment represents a reconstruction of the MRCA's DNA, and that this DNA may thus be compared to all sets of DNA, including other reconstructed MRCA DNA, thus potentially leading to more DNA matched Users, or DNA matches between Ancestors; and, ii) the flow of a DNA segment from the MRCA to each of the DNA matched Users may be predicted, and that the said DNA segment may be associated to each descendant between the MRCA and the respective DNA matched Users' who matched with the DNA segment; and, iii) the flow of Y DNA and mtDNA, if available, may be restricted to the paternal and maternal branches respectively, and associated to all the ancestors which lie on the respective paternal or maternal path between two DNA cousins who share the segment, and that if the Ancestors in the trees of two DNA matched Users are connected in a Competitive Neural Network by connections to equivalent Y and mtDNA nodes, then Ancestors who share the same haplogroup will be attracted in said Competitive Neural Network; and, iv) that if a User has a set of DNA matches to other Users, and if a sub-set of those DNA matches have segments which overlap (matching) each other on a continuous length, then in this system (100) the overlap of each pair of Users may be recorded in an associative ICW-DNA node, such that each such pair of Users may have their respective MRCA drawn toward, in the associative neural network, the Ancestors that any segment gets assigned to by an MRCA assignment; and, h. address the problem, that there are many sub-trees of well curated relationships in various family trees and that the good vetted data of one tree that could solve a problem for a User with another family tree, is not readily available to the Users and that this system (100), by recording the User's family trees into light-weight meta-data Virtual Family Trees, and by capturing the well-curated data of all family trees into a set of light-weight meta-data Virtual World Trees, affords the AI and ML systems in the holistic set of sub-systems, the ability to explore possible connections between Ancestors in different family trees by having a multiplicity of Agents building Tentative sub-trees or by having Agents creating Speculative Ancestor nodes to connect sub-trees which have significant evidence of relationship supported by the DNA matches between Users and other associations collected by the system. 3. The system of claim 1, wherein said system (100), herein also called the ‘holistic system’, receives and acts on a plurality of inputs comprising: a. a plurality of genealogic family trees, which may be loaded by GEDCOM import, which codify the ancestry of a plurality of participating Users; b. a plurality of genetic data sets comprising the genomic sequencing of single nucleotide polymorphisms (SNPs), or any part of the genome of the Users, wherein each User will have obtained this genetic data from a genomic sequencing service and will have uploaded it to their respective ‘member DNA data’ databases (DB or DBs in the plural) in their respective User accounts in the system, wherein the format of the genomic data will be in a standard format such as ‘human reference build 37’; c. a plurality of relationship estimations between various Users as calculated by 3rd party systems, based typically on the lengths of DNA segments shared between pairs of Users, wherein the relationship estimation may include: i) an estimation of the Genetic Distance in term of generations between each pair of Users, usually stated in terms of degrees of separation by cousinship; ii) an confidence rating of the relationship estimation; iii) information describing the location and lengths of the shared DNA segments between pairs of DNA matched Users; d. a plurality of supporting evidences and attributes for the elements of a User's family tree, or a databases access to those family trees in order to derive the evidences and attributes assigned to each ancestor and relationship in a User's family tree on a 3rd party service provider; e. a plurality of historical, genealogic, and journalistic data as retrieved by sub-systems of the invention searching various public databases, or 3rd party databases as permitted by arrangements with those 3rd parties and sources; 4. The system of claim 1, wherein said system (100), processes the inputs and derived data to create or modify data comprising: a. a plurality of ‘Virtual Family Trees’ (VFTs) illustrated in FIG. 11), each constructed of a plurality of ‘Virtual Individual Ancestor’ (VIA) nodes, each of which may have a plurality of connections to parents and/or children, such that each VFT is a lightweight data-structure to represent at least a User's full pedigree out to the maximum number of generations that a DNA supported MRCA may occur at according to the Users' DNA matches list; b. a plurality of ‘MRCA Virtual DNA’ (MRCA-Vdna or just MRCA) nodes which are allocated to a first User's account, the nodes of which each represent one or more propositions for the putative MRCA between two User's, the two Users being a first User and a second User who have been predicted to be related by DNA matching, wherein each MRCA node is initialized with bi-directional pointers between it and the VIA nodes in the owning first User's VFT that fall within the estimated Genetic Distance range of the predicted relationship between the first User and second User, as further described and illustrated in FIG. 12) and its' discussion, and such that each MRCA node will initially be a placeholder, and as analysis progresses, the eligible bi-directional links between it and the VFT VIA nodes will decay or enhance their connection weights, and that some will die off (be deleted) as they pass below a threshold, effectively reflecting that the probability that the VFT VIA node is not the MRCA between the two DNA matched Users; c. a plurality of ‘Virtual Attribute Nodes’ (VANs) which represent evidences and attributes associated to the ancestors represented in said VFTs, and which are used to create part of a Competitive Neural Network, wherein said network is comprised of nodes and interconnections, wherein said interconnects are weighted to represent the probability that the two connected nodes are associated, and the weights regulate activation passed between nodes according to various algorithms and sub-systems described and claimed in the invention, and, wherein said VAN's have built-in to their data the connections to other nodes, and the VAN's may be stored on Local Shared Attributes DB's if only related to at most two VFTs, otherwise they may be copied to a Global Shared Attributes DB, which shares a bi-directional pointer to the copy in the Local shared attribute DB; d. a plurality of ‘Virtual Ancestor Records’ (VARs) which record or point to (as in a record pointer) the supporting evidences and attributes and their confidences and weights, related to each VIA node; e. a plurality of ‘In Common With’ nodes of various types, which represent results of complex analysis by the sub-systems and Agents, and which connect to and define a subset of the previously mentioned Competitive Neural Network in the manner of VAN's, wherein, examples include the ICW-Cell node, which points to all the ICW-DNA nodes of a particular individual, and ICW-DNA nodes which represent segments of DNA shared between Users and their MRCA Ancestors; f. a plurality of ‘Chromosome Maps’ along with a set of ICW-DNA nodes pointing to their respective DNA segments in the respective chromosome map DB, wherein each VIA node (putative ancestor or individual) in each VFT will have an associated chromosome map after at least one DNA segment has been triangulated to that VIA node, as a result of various sub-systems which make such assignments of MRCA to VIA nodes, wherein such chromosome maps do not hold complete DNA data, but rather only hold the indicia of DNA segments as stored securely in a User's DNA database, or a created Ancestors' DNA database. 5. The system of claim 1, wherein said holistic system executes the various sub-systems, algorithms and methods described herein, with results comprising: a. a plurality of ‘Virtual Ancestor Records’ (VAR), nodes and connections updated with automatically calculated or manually entered confidences and weights; b. a plurality of ‘MRCA Virtual DNA’ nodes with updates on connections to their sets of eligible VIA nodes, including pruning of some connections or variation in the weight of various connections from the MRCA node to eligible VIA nodes, according to the outputs of the sub-systems which ran the relevant analysis, and including possible connections to ICW-DNA nodes and ‘Trait X’ nodes; c. a plurality of additions or modifications to the set of virtual ‘attribute’ nodes (VANs), their properties, connections, or state; d. a plurality of additions or modifications to the ‘Virtual Family Trees’ (VFTs) of various Users according to the work of the various sub-systems which interact with them; e. a plurality of additions or modifications to one or more Virtual World Tree (VWT) according to the work of the various sub-systems which interact with it; f. a plurality of additions or modifications to the Chromosome Maps and ICW-DNA nodes of various VIA nodes in either VFT's or VWT's, according to the work of the various sub-systems which interact with them; g. a plurality of graphical user interface (GUI) representations of the data generated, comprising: i) displays of the Users' VFT pedigree as illustrated in FIG. 14), along with display of MRCA assignments to VIA nodes, ii) display of two VFT pedigrees facing each other as illustrated in FIG. 13), along with display of VFT paths from the MRCA assignment(s) VIA to the respective Users VIA node; iii) display of a VFT VIA's VAR record values, including a weight ‘W’ and confidence metric ‘P’ for each attribute, as illustrated in FIG. 15); iv) display of a reduced VIA node's VAR record as illustrated in FIGS. 17) and (18), with automated display of the Ancestor's country of birth flag, automated display of the Ancestors country of death flag, and automated display of an DNA icon if the Ancestor has DNA triangulations, and the count of said triangulations shown in the image, along with other items displayed such as counts of ICW-A, ICW-M matches; v) a display of the ICW-A feed-forward network and state of nodes, as described in FIG. 21), sub-system (2100); vi) a display of a DNA segment alignment and overlap and MRCA ordering viewer, as described in sub-system (2700); vii) a DNA segment flow graph viewer, as described in sub-system (2800); viii) a graphical display of a Competitive Neural Network, as illustrated in FIGS. 30) and (31), sub-system (3000); ix) an annotation of ICW Disembodied Cousins icons to User's VFT to facilitate visualization of fan-up and fan-down clusters; x) an ‘Interactive Migration Map with Vectors and Sliding Time Scale’; xi) an MRCA Vdna Star Browser tool, as illustrated in FIG. 42), sub-system (4200); xii) an ICW-Match automated graphing system, as illustrated in FIG. 43), sub-system (4300). 6. The system of claim 1, comprising a networked computer system having at least one computer display device, at least one processor device, at least one database and storage media having computer-executable instructions configured to programmatically execute the methods on the data and produce outputs, wherein said networked computer system, in the preferred embodiment consists of a distributed computer system connected by a network as illustrated in the block diagram of FIG. 40), wherein one embodiment of the primary hardware and database components are described therein, and the architecture being distributed with the intent that an Agent based system may execute a plurality of computer programs called Agents herein, which communicate with each other through ‘Agent Exchanges’ (904), which are controlled by an Agent Control System (900) and through direct peer-to-peer message passing interface over the network, or through normal ICP (inter-process communication on Unix). 7. The system of claim 1, which is in part comprised of a set of lightweight data structures used by all sub-systems, those data-structures forming parts of the elements of the Competitive Neural Network system, and those data structures comprising, but not limited to: a. a plurality of Virtual Ancestor Record (VAR), as described in sub-system (1700) and FIG. 17), maintain meta-data of the biographic information related to an individual, wherein any evidence related to an individual, his/her relationships, travels, ownership etc., may be named in this record, should get a confidence measure, and should point to its originating source if any exists, and wherein the VAR will also contain internally derived data, such as connections to various other nodes, and their confidences; b. a plurality of Virtual Individual Ancestor (VIA) nodes, as introduced above, wherein a VIA node either describes a specific individual (usually an Ancestor), or is a placeholder in a User's VFT pedigree for an Ancestor who must have existed (if in the pedigree), or is speculated to have existed (if in filling a gap in a speculative tree), and wherein a VIA node contains a VAR which has a plurality of fields to define all biographic information about the individual represented by the VIA, and wherein a VIA node may also point to a ‘Chromosome Map’ database, which stores all DNA segments that have been associated to the individual, either through 3rd party sequencing, or through the process of MRCA discover, and such that the root node of a VFT will always have a chromosome map database, and such that a VIA node may have a pointer to the owning User's external family tree node, and such that a VIA node, like all nodes, has a record for simulations in which Agents may write their information regarding ID, activation and other items; c. a plurality of Virtual Family Trees (VFT), wherein in one embodiment of the invention methodology, a VFT pedigree is automatically created for each participating individual (User), with each ancestor represented by a Virtual-Individual-Ancestor Node (VIA), wherein the VIA nodes and pedigree network for an individual participant are created extending back a sufficient number of generations to encompass the initial reach of genomic analysis, such that this virtual family tree is a scaffold, designed to provide a light-weight data structure to hold information relevant to nodes (ancestors), and their connections (relations), and the connections' feasibility weights, wherein nearly every VIA node will be an eventual MRCA, so as a placeholder, it serves as a reference and linking point for various algorithms which attempt to associate MRCA's to VIA nodes; d. a plurality of Virtual World Trees (VWT), being an amorphous network comprised of VIA nodes and connections, which serves the purposes of a general, shared family tree to which various Agents share high quality family tree information through ‘VWT tending Agents’, and whereby special ‘Speculative Search Agents’ as described in sub-system (3500), may use search algorithms to attempt to find high quality paths between Ancestors in different VFT's and/or the associated VWT sub-graphs, and if found, will stitch the discovered connections into the associated VWT and then share with the various VFT's such that they may enhance their respective trees; e. a plurality of Virtual Attribute Nodes (VAN's), which represent any characteristic or information that may be in common between Ancestors, Users or their DNA, such as a particular surname, ethnicity, or place visited or lived in; f. a plurality of Local and Global Shared-Attributes DB's and represented Networks, wherein a plurality of VAN's are stored in the databases; g. a plurality of ‘In Common With’ (ICW) nodes of various types, which represent characteristics or information shared between Ancestors or Users, such as two Ancestors being the same person in different trees, and two User's sharing a common DNA match to a third User; h. a plurality of sets of MRCA Virtual DNA (MRCA-Vdna, or MRCA) nodes per User, representing place-holders of the DNA-match between two Users, such that each MRCA node will initially be linked to every potential VIA node candidate in each of the two User's VFT's, and the weights on the links will be normalized with respect to the number of links, and such that initially they are set to 1/ (number of links) such that each link initially has equal likelihood of being the MRCA between the two VFT's. 8. The system of claim 1, which is in part comprised of, a set of main computer programs running on one or more computers and managing a plurality of databases, as illustrated in FIG. 40) and described as sub-system (4000), which in general, a. Create and manage a plurality of shared databases; b. Create, Initialize and monitor a plurality of ‘Agent Exchanges’, which are described in the sub-system (900) ‘Agent Control System’, which is variably called the ‘Agent Management System’ (906); c. Schedule and initiate primary program sequences as illustrated and described in system (100); d. Perform all tasks of conventional modern computers, such as reading and writing data to short and long term storage media, processing that data according to the instructions of various programs, display that data onto visual media as requested. 9. The system of claim 1, which is in part comprised of a sub-system (200) ‘New User Initialization System’, which itself comprises: Create, initialize and manage a plurality of User accounts, including creation of a new User's Account, Profile, VFT scaffold, loading and constraint checking of Evidences along with initial confidence estimations per sub-system (1100), register User's DNA matches, create User's ‘Chromosome Map’ Db, create User's local shared attributes DB., and create User MRCA-Vdna nodes one per DNA matched User in the first User's set of matches, wherein create of said MRCA-Vdna nodes also includes their initialization process in sub-system (1200). 10. The sub-system (200) of claim 1, which is in part comprised of a sub-system (1100) ‘User VFT create and setup’, wherein the Virtual Family Trees (VFT), in one embodiment of the invention methodology, is in part a pedigree of each participating individual (User), with each ancestor represented by a Virtual-Individual-Ancestor node (VIA), and the VIA nodes and pedigree network for an User are created extending back a sufficient number of generations to encompass the initial reach of genomic analysis (the distance in generations to the furthest predicted MRCA), and this virtual family tree is a scaffold, designed to provide a light-weight data structure to hold information relevant to nodes (ancestors), and their connections (relations), and the connections' feasibility weights, and such that each VIA node is lightweight, meaning using minimal memory, and not holding any large data files such as images, documents or DNA, and each VIA node is initialized with any available meta-data from the corresponding Ancestor in the User's primary family tree, wherein the biographic information is summarized on the VIA node, including such items as names, data of birth, residences with place and date, etc., and such that the original digitized records are not copied into the VIA node, but rather, pointed to by pointers from the related fields in the VIA nodes' VAR record, and such that upon completion of the basic creation phase, the ‘Confidence Agents’ and ‘Constraint Agents’ are activated on the VFT to generate initial values and estimates for confidences and whether items and relationships pass basic constraints, and furthermore the description of sub-system (1100) from FIG. 11) is included here. 11. The sub-system (200) of claim 1, which is in part comprised of a sub-system (1200) ‘Create User MRCA Vdna Nodes’, wherein each MRCA-Vdna node first points to the record defining the DNA relationship between the first and second User, then it determines the genetic range of the probable ancestors based on the information obtained from sequencing it and makes bi-directional connections to the VIA nodes of the first User's VFT that fall within the estimated Genetic Distance, and wherein each connection will be given an initial strength (weight) equal to 1/(number of candidate nodes), such that each VIA node has equal likelihood of being the MRCA Ancestor, and wherein it will also point to the DNA segment shared between the two Users which should be stored in the User's chromosome DB., and at some point the MRCA-Vdna will point to an ICW-Cell node, and wherein the sub-system (1200) illustrates the concept of a plurality of MRCA Nodes by a VFT, and wherein the description of sub-system (1200) is included herein. 12. The system of claim 1, which is in part comprised of a sub-system (300) ‘Continuous accumulation of genealogic evidences’, which consists of data input manually or collected automatically by Agents from external sources, and: a. wherein a User may input data directly into their personal family tree, which will then be linked to by the respective VAR field in the respective VIA node, and a confidence measure will be assigned to the new data item, either by the User or by a VFT Agent, or by a ‘Confidence Agent’, or by a ‘Constraint Agent’, each of which are run at various times by their respective sub-system flows, and b. wherein, User's data input, or other sub-systems data input, registers triggers in the Agent Exchanges, to cause the appropriate sub-system Agents to act on the new data found in a User's VFT, VIA nodes and VARs, and c. wherein all new genealogic evidences, including biographic information, are individually saved to VAN's by either creating a connection to an existing VAN, or by creating a new VAN node, and then creating a connection to that node, and d. wherein the connection to the VAN is given a weight proportional to the confidence in the data's relevance and viability. 13. The system of claim 1, which is in part comprised of a sub-system (400) ‘Data-mine User's own and User's Matches’ Trees', which comprises according to the figures and their respective descriptions, a computer program (usually involving an Agent Exchange) running a plurality of sub-systems listed here, which themselves automatically operate on the structures and elements of the Competitive Neural Network system, including the VFT's of Users, the general VWT, and the attribute network, wherein additions and modifications to these structures and elements act holistically to capture associations, inferences, constraints, confidences and dependencies, and such that the plurality of sub-systems comprise in part: a. the sub-system ‘Find, Record: General Attribute Commonalities’ (as described in 402), which in effect, entails connecting a VIA's VAR record field for each attribute to an VAN and creating a weight for the connection according to the confidence in the association or viability of the attribute; b. the sub-system ‘Find, record ICW Ancestors’ (as described in 404), which employs ICW-A Search Agents' as described in one embodiment in block-diagram (2000) and sub-system (2100), will compare VIA nodes from the two trees of two DNA matched individuals, comparing such things as their surnames, place of birth, date of birth and death, and wherein the system will use intelligence to sort the candidates to ensure that VIA nodes compared had lived in overlapping life-times, and wherein this evaluation will entail use of the constraints Agents to ensure that individuals tested have compatible properties, and furthermore the comparison will use the ‘Proximity Search Agents’ (420), which will ensure they lived in the same general time and place; c. the sub-system ‘Evaluate ICW Ancestors’ (412) which runs the confidence analysis sub-system (1500) on each Common Ancestor discovered; d. the sub-system ‘Queue ICW Ancestors to VWT’ (414), which thus registers any ICW-A matches to the Virtual World Tree, wherein registration is done through the Agent Exchanges (AX), and wherein the VWT Tending Agents are launched when such a job is queued with an AX; e. the sub-system ‘Find, Evaluate ICW Matches’ (406); f. the sub-system ‘Evaluate MRCA-Known ICW Matches’ (408); g. the sub-system ‘Run any sub-stage data through the MRCA Assignment Engine’ (410); h. the sub-system ‘Run ICW-A Search Agents’ (418); i. the sub-system ‘Run Common Match Cluster Agents’, (416); j. the sub-system ‘Run Proximity Search Agents’ (420); k. the sub-system ‘Run Attribute Search Agents’ (422), which data-mine attributes common between the Ancestors of User's trees and registers them in the Shared Attributes DB, wherein a shared attribute is saved in a VAN, and connected to the field of the attributed in the VIA's VAR (virtual ancestor record), and where each Ancestor's attributes, if found to be shared with any other Ancestors (VIA nodes), will be associated with a VAN node in the global shared attributes database, and thus, each attribute that is shared forms a cluster center of VIA's which share that attribute; l. the sub-system ‘Run Cluster Mining Agents’ (424), and which invokes the sub-system (938). 14. The system of claim 1, which is in part comprised of a sub-system (500) ‘Continuous evaluation of tree and data quality and Constraint Checks’, which is comprised of the following sub-systems, which are each triggered by sufficient accumulation of changes in their respective domains, and which are controlled by the ‘Agent Management System’, including ‘Agent Exchanges’, and which are comprised of in part: a. a ‘User Confidence Input Editor’, which allows User's to enter or modify automatically generated confidences, and which afford Users an ability to vote on validity or relevance of records associated to an Ancestor in the VWT, in order to assign it a consensus confidence metric; b. an ‘Evaluate User tree and data Quality’, represents the changed-data triggers evaluation to send to the Agent Exchange, to launch appropriate Agents; c. an ‘Constraint Satisfaction Agents Launch’ as detailed as sub-system (1600); d. an ‘Confidence Agents Launch’ as detailed as sub-system (1500); e. an ‘VFT Annotation Agents Launch’ as detailed as sub-system (1700); f. an ‘VWT Annotation Agents Launch’ as detailed as sub-system (1800); g. an ‘Record Confidences to (232) Member Ancestors Trees’ which writes to the databases (242) Virtual Family Trees, (244) Virtual World Tree, as detailed in sub-system (1900). 15. The system of claim 1, which includes a distributed Competitive Neural Network (CNN), which enables the discovery of highly associated or similar entities in different parts of the network (such as Ancestors in two different family trees), and which is illustrated in FIG. 30) and FIG. 31), and which consists of nodes and connections between them, wherein the nodes of the CNN are comprised any Nodes created in the system (100) and it's sub-systems, including Virtual Individual Ancestors, Virtual Attribute Nodes (VANs) containing attributes shared between Ancestors, Virtual DNA nodes to capture the relationship of DNA between Ancestors, and various ‘In Common With’ (ICW) nodes to capture various commonalities including two Users who both match a third User, and common Ancestors found in DNA matched cousin's trees which provide hints that these User's may lead to the MRCA between the two Users, and wherein the ‘connections’ between the various Nodes of the CNN represent the probability that the two connected nodes are associated, and wherein the connections are weighted to reflect the confidence and importance of the association between the Nodes, and wherein the weights regulate activation passed between nodes according to various algorithms and sub-systems, and wherein the connections are not physical connections, but rather virtual, in that messages and activations are mediated by Agents which follow the pointers between nodes, and deliver a packet of data of the network, with the packet representing information such as the activation or inhibition sent, the type of signal sent, the nodes visited in between, constraints that are relevant to the packet, and the decay period, to name a few, and wherein said VANs have built-in to their data the connections to other nodes, and the VANs may be stored on Local Shared Attributes DB's if only related to at most two VFTs, otherwise they may be copied to a Global Shared Attributes DB, which shares a bi-directional pointer to the copy in the Local shared attribute DB. 16. The system of claim 1, which is in part comprised of a sub-system (600) ‘Accumulate all desired data into the Competitive Network system’, which is periodically run by means of their respective Agents communicating to the Tending Agents (920) of the VFT and VWT, wherein the activity may simply be an update of connections and weights, or may results in an extraction of the network into sparse arrays for supercomputer analysis, and wherein The shared various data elements from various collection agencies such as those shown in state (602), may be ‘extracted’ into their relevant DB's (604), and stitched into a ‘Competitive Network’ (606), and global Inter-Match network (608), wherein the ‘Competitive Network’, in one embodiment, is basically the holistic combination of the existing Virtual Family Trees, their connections to Local and Global Shared Attributes DB nodes (and the attribute Clusters built therein), and their connections to MRCA Vdna nodes, and thus the competitive network strives to embody all evidences which could guide the User and System in sorting out which Ancestor(s) associates to which MRCA(s), and wherein some of the evidence sources input to the competitive network include: (401) Attribute Commonalities, (412) ICW Ancestor Connections, (408) ICW User Matches Connections, (810) Disembodied Cousin Influences (by ICW-DC nodes), (1000) DNA Mapping Influences, (812) VWT Influences and Connections, and (3600) Migration Proximity Influences via ICW-Proximity Attribute Nodes (ICW-Ps), as described in sub-system (4900). 17. The system of claim 1, which is in part comprised of a sub-system (700) ‘Run concurrent MRCA assignment optimization’, as described in the FIG. 7) and its' explanation, with the methodology comprising: a. for a small set, easily computed on a single multi-core workstation, the ‘MRCA Engine’ may be employed, and; b. for a larger set, perhaps involving hundreds or thousands of Users who have been found to have a high-density of interconnectedness, a distributed implementation of the ‘MRCA Engine’ is used, wherein activation packets are sent between ‘nodes’ via a network protocol such as TCP/IP or UDP datagrams, and; c. for a global analysis involving thousands or millions of Users, and when a large compute farm or cloud is available, the Users' VFTs and the global attributes DB may be converted to an Inter-Match Network (608), and then to distributed sparse matrices in sub-system (4900) FIG. 49), and such that operations are executed on the sparse matrices in parallel or asynchronously, and; d. for a global analysis involving a plurality of thousands or millions of Users, the several algorithms in the sub-system (4800), General N-Cluster and MRCA Assignment Algorithms, may be used, and; e. for an on-going DNA flows based analysis spanning across all Users on a distributed computer network (ie, the internet), the sub-system (5000) ‘Global DNA Cluster Generation and Analysis with Competitive Neural Networks’ is employed. 18. The system of claim 1, whereby after the results of an execution of sub-system (700) ‘Run concurrent MRCA assignment optimization’ are obtained, for each successful MRCA assignment, confidence enhancements are propagated from the MRCA VIA node down the direct DNA flow path to the User, in all VFT's which have a VIA node connecting to said MRCA VIA assignment node, and thus, if two User's have a successful determination of their MRCA to a VIA node X, then the connection and other confidences from that VIA node X, down to each User in their respective VFT trees are enhanced, and furthermore, if an MRCA node has been merged with other MRCA nodes, indicating a plurality of Users' have successfully triangulated to the MRCA node, then the confidences in paths are proportionally enhanced, and the enhancement of each connection or VIA node is regulated by its' initial confidence, such that if a node had very low confidence, it will get very little enhancement, and if a node or connection has maximal confidence (100% or 1.0), it will get no further confidence enhancement. 19. The system of claim 1, whereby after results of each execution of sub-system (600) are obtained, for each successful MRCA assignment, the system will dispatch various Agents to automatically propagate confidences of discovered MRCA's from descendants across all involved trees (ie, DNA matched Users' trees) into a common tree such as a Virtual World Tree. 20. The system of claim 1, whereby after the results of an execution of sub-system (700) ‘Run concurrent MRCA assignment optimization’ are obtained, any successful MRCA assignments results are annotated to VFT VIA nodes by ‘Tree Annotation Agents’ from sub-system (1700), such that same may be easily viewable by Users, as illustrated in FIG. 14), wherein the marker appears like a ticker-tape with annotation to show the level of confidence in the Ancestor according to the number of DNA MRCA's connecting to it. 21. The system of claim 1, wherein a sub-system (1300) ‘MRCA Assignments Display’, in which the VFT of two DNA matched Users' who have found an MRCA, will be displayed as shown, with the pedigrees of each starting from the edge of the screen and expanding towards the middle of the screen, such that the path of the DNA flows can be shown. 22. The system of claim 1, whereby after a results of sub-system (600) ‘Accumulate all desired data into the Competitive Network System’ are obtained, for each successful MRCA assignment, ability to automatically share high quality ancestors from one DNA Users' triangulation-confirmed pedigree to those of DNA cousins who share some or all of that pedigree, or who have paths to the ancestor associated with the MRCA, through a shared ‘Virtual World Tree’ (VWT), wherein the sharing of high-quality Ancestors is done by VWT Tending Agents described which traverse a User's tree, looking for equivalent Ancestors in the VWT, and if found, and if they VWT ancestor is better, updating the User's VFT node, or on the other hand, if the User's version of the Ancestor is better, then updating the VWT with the improved information, and if the two have significant contradictions, adjusting the confidences to reflect the reduced certainty. 23. The system of claim 1, which is in part comprised of a sub-system (800) ‘Continuous exploration and growth of virtual trees’, including: a. propagate enhanced confidences from new MRCA assignments to the descendants of the MRCA who lie on a path between DNA matched Users who have the MRCA, b. evaluate Queued ICW Ancestors to add to VWT, c. evaluate Queued Speculative Trees for addition to VWT, d. evaluate if Users' VFT Trees should inherit enhanced sub-trees from VWT, on User option, e. evaluate and explore Disembodied Cousins, is detailed in sub-system (3300), (3400), f. dispatch Virtual World Tree Tending Agents as detailed in sub-system (1800) and (2200), g. dispatch Speculative Tree Search Agents, as detailed in sub-system (3500), h. assimilate discoveries from all the various search systems, on all trees, and integrate them in a manner which propagates the inherent constraints and confidences, as discovered by many Users, into the VWT. 24. The system of claim 1, which is in part comprised of a sub-system (900) ‘Agent Control System’, which is equivalently called the ‘Agent Management System’, which is comprised of, in part, a set of light-weight computer programs described as ‘Agents’, running on a single monolithic system, or alternatively, on a set of distributed networked computer systems, with the general purposes of the various Agents comprising in part to calculate, record and display indicators of likelihood of relatedness of virtualized individuals and their ancestors in the software data structures described in the invention according to several methods described in various sub-systems, and to calculate, record and display various metrics of confidence on genealogic data and inferences associated with virtualized ancestors, using several methods described regarding Agents herein, and wherein the Agent systems are comprised of: a. sub-system (922) ‘Attribute Agents’, which run data mining on VFT's to find common attributes, not focused on ICW-A matches, and store in a local or global shared attributes DB (428); b. sub-system (916) ‘Confidence Agents’, which are in part comprised of a sub-system (1500) ‘Confidence and Constraint Agents Launch’: c. sub-system (918) ‘Constraint Agents’, which are in part comprised of a sub-system (1600) ‘Constraint Satisfaction Calculating Agents’; d. sub-system (920) ‘Virtual World Tree Tending Agents’; e. sub-system (934) ‘Virtual Family Tree Agents’; f. sub-system (924) ‘Migration Proximity Search’ Agents; g. sub-system (926) ‘Tree Probability Agents’; h. sub-system (928) ‘In Common With Match Agents; i. sub-system (930) ‘In Common With Ancestor Agents’ which evaluate the likelihood that two VIA nodes represent the same individual, by means of a custom neural network, wherein the VIA nodes are one each from the VFT of two DNA matched Users; j. sub-system (938) ‘Cluster Agents’. 25. The system of claim 1, which is in part comprised of a sub-system ‘MRCA Assignments Displays’, which includes the several sub-systems depicting the assignment of MRCA's to VIA, comprising: a. sub-system (1300), which allows the User to see two pedigrees simultaneously, and whose description is by reference included here in full; b. sub-system (1400), which displays DNA icons next to triangulation confirmed VIAs, the description of which is by reference included here in full; c. sub-system (1700), which has an icon for DNA triangulation count, whose description is by reference included here in full; d. sub-system (2700), which displays a Chromosome Map with MRCA's pointing to associated segments, and with special actions when any segment is clicked, as described in the description of sub-system (2700) and FIG. 27); e. sub-system (3100), which displays MRCA connections to VIA's according to a current estimation of probable assignment to a VIA; f. sub-system (4200), which expands an MRCA which has a multiplicity of DNA triangulations, into the MRCA nodes seen by owning Users. 26. The system of claim 1, which is in part comprised of the sub-system (1700) represented in FIG. 17) as an illustration of the information display of one example node from a Virtual Family Tree, in one embodiment, with the description of ‘system (1700)’ included here in full, and that: a. this claim provides a computer automated visibility into confidences intended by those researchers (‘Users’) when viewing their personal family trees, and, b. this claim provides a unique ability to automatically tag an ancestor profile or sub-tree as ‘speculative’, or ‘placeholder’, or ‘missing-link’. 27. The system of claim 1, which is in part comprised of the sub-system (1800) represented in FIG. 18) as an illustration of the ‘Statistics View’ elements as related to a Virtual Family Tree node, in one embodiment, with the description of ‘system (800)’ included here in full. 28. The system of claim 1, which is in part comprised of the sub-system (1900) represented in FIG. 19) as an illustration of the relationship of confidences (usually decreasing) going up a branch of the VFT, in a form of Bayesian Belief Network, in one embodiment, with the description of ‘system (1900)’ included here in full. 29. The system of claim 1, which is in part comprised of the sub-system (2000) represented in FIG. 20) as a flowchart and illustration of the operation of In-Common-With Ancestor discovery and integration, in one embodiment, with the description of ‘system (2000)’ included here in full, and that, this claim provides a unique automated ability to easily find, link to, and cooperatively analyze in-common-with ancestors (ICW) across DNA matched Users' trees, with benefit of the holistic system described. 30. The system of claim 1, which is in part comprised of the sub-system (2100) represented in FIG. 21) as an illustration of a Neural Network for In-Common-With Ancestor discovery via pattern matching, in one embodiment of the Ancestor matching AI algorithms, with the description of ‘system (2100)’ included here in full, which compares two ancestors to determine likelihood that they are the same person, by taking as inputs into the first layer of inputs, called the ‘Parsing and Feature Extraction’ layer, the key information from the Ancestors VAR records, processing that information with Constraint Agents and using the Fuzzy Logic DB, and then passing this refined data to a first layer of neurons, which then feed the information forward to other layers, and to neurons in the compared Ancestors data path, and onward through several hidden layers of neurons and connections, until the output is a probability measure of whether the two are the same individual, and that the nodes, connections and processing in the neuron nodes will have been trained by feeding it examples from manually vetted family trees, wherein if two Ancestors are known to be the same person to some level of confidence, but have somewhat different information, the neural net will be trained by modulating weights of connections through backpropagation until the output correlates to the confidence given for the Ancestors. 31. The system of claim 1, which is in part comprised of the sub-system (2200) represented in FIG. 22) as an illustration of a ‘Virtual World Tree’ Tending Agent harvesting commonalities between two trees to grow the VWT, in one embodiment, with the description of ‘system (2200)’ included here in full. 32. The system of claim 1, which is in part comprised of the sub-system (2300) represented in FIG. 23) as an illustration of initial MRCA-Vdna VIA candidate set assignment for one pair of DNA matched Users, in one embodiment, wherein the MRCA Vdna set is as set of pointers to the set of Ancestors which could be the MRCA, given the predicted relationship of the two DNA matched Users for whom the MRCA Vdna is a placeholder for their MRCA, with the description of ‘system (2300)’ included here in full. 33. The system of claim 1, which is in part comprised of the sub-system (2400) and (2500) represented in FIGS. 24) and (25) as an illustration of reduced MRCA-Vdna VIA candidate set assignment for one pair of DNA matched Users, in one embodiment, wherein the set of eligible, or likely, Ancestors has been reduced by various means and algorithms built into the holistic system, including DNA steering by ‘chromosome mapping’, or by the combinatorial assignment algorithms, or by the ICW matching algorithms, or by others, with the description of ‘system (2400)’ included here in full. 34. The system of claim 1, which is in part comprised of a sub-system (932) ‘DNA Agents’, which are described in the sub-system (900) ‘Agent Control System’, (1000) ‘DNA Mapping Influences’, (2600) ‘Referencing Shared Segments to each Ancestor in the DNA Flow’, (2800) ‘DNA Segment Flow Graph Viewer’, (3100) ‘MRCA Engine’, and sub-system (5000) ‘Global DNA Cluster Generation and Analysis with Competitive Neural Networks’, wherein the DNA Agents populate Ancestors' nodes with pointers to the DNA segments that have been putatively associated to the Ancestor, and as an Ancestor's DNA inventory increases, with potentially overlapping DNA segments re-creating the genome of the Ancestor, the Ancestor's DNA is presented to the DNA matching algorithms such that User's may match directly to the Ancestor, or Ancestor's may be matched to each other, and furthermore, that such a continuation of accumulation of DNA and recycling it into the matching system as a new User, creates the potential to generate matches from Ancestors born many hundreds of years ago. 35. The system of claim 1, which is in part comprised of the sub-system (2600) represented in FIG. 26) as an illustration of DNA Mapping Agents assigning DNA segments to VFT VIA nodes, in one embodiment, with the description of ‘system (2600)’ included here in full, and, this claim provides a unique ability to automatically incrementally recreate ancestors' genomes from all MRCA's and to automatically re-use those virtual ancestors in the general matching system as a regular User, but with only partial DNA. 36. The system of claim 1, which is in part comprised of the sub-system (2700) ‘DNA Map System for each ancestor, to show overlaps’, represented in FIG. 27) as an illustration of the generation of a stacked chromosome map with links from DNA segments to associated MRCA Vdna nodes, in one embodiment, with the description of ‘system (2700)’ included here in full, which postulates that the IBS shared DNA data is at least beneficial in this system to attracting Ancestors who are ethnically close, and which provides improvements over prior art by providing a system to automatically map shared genome data according to most likely ‘most recent common ancestors’ (MRCA's), and inversely, from all MRCA's to a chromosome/surname map, which is commonly called ‘chromosome mapping’ in prior art, and wherein this is enabled in a manner in this invention such that the Users need not publicly expose their actual DNA information to other Users, as the work is done securely within the confines of the programs, and information that is sent over networks is encrypted. 37. The system of claim 1, which is in part comprised of the sub-system (2800) represented in FIG. 28) as an illustration of a DNA segment flow graph viewer, in one embodiment, with the description of ‘system (2800)’ included here in full, wherein this display allows a User to trace the flow of one or more DNA segment from one or more MRCA through their family tree. 38. The system of claim 1, which is in part comprised of the sub-system (2900) represented in FIG. 29) as an illustration of Y and mtDNA specific MRCA-Vdna candidate set adjustment for one pair of DNA matched Users, in one embodiment, with the description of ‘system (2900)’ included here in full, wherein the Ancestors in the trees of two DNA matched Users are connected in an associative network by connections to equivalent Y and mtDNA nodes, such that Ancestors who share the same haplogroup will be attracted in the Competitive Neural Network. 39. The system of claim 1, which is in part comprised of the sub-system (704) ‘MRCA Constraint Satisfaction and Assignment Optimization Engine’, which is comprised of the following sub-systems: a. the sub-system (3000) represented in FIG. 30) as an illustration of an embodiment of the MRCA Engine' Competitive Network with Virtual DNA nodes connected to VFT nodes, with the description of'system (3000)' included here in full; b. the sub-system (3100) represented in FIG. 31) as an illustration of an embodiment of the MRCA Engine' Competitive Neural Network with Attribute nodes connected to VFT nodes, with the description of'system (3100)' included here in full; c. the sub-system (3200) represented in FIG. 32) is a flowchart of one embodiment of the MRCA Engine process of local and global optimization of MRCA assignments, with the description of ‘system (3200)’ included here in full; d. the sub-system (4100) represented in FIG. 41) as an illustration of the abstract visualization tool for visualizing an ‘MRCA Engine’ network stimulation and settling states, in one embodiment, with the description of ‘system (4100)’ included here in full; e. and whereas the above sub-systems cooperatively and holistically provide a unique automated ability to use various data points shared across DNA matched User's trees to focus MRCA search efforts, including documents shared between Ancestors of different pedigrees. 40. The system of claim 1, which is in part comprised of the sub-system (3300) Disembodied Cousin evidence accumulation and Triangulation method, in one embodiment, with the description of systems (3300) and (3400) included here in full, and which also comprises; a. for every DNA matched pair of cousins, a scan is made of their trees (connected paths from the DNA cousin), and for each pair of ancestors who meet a criteria of ICW similarity such that they could be the same person, an ICW-DC (In-Common-With Disembodied Cousin) node (3306) is created connecting the two, and the ancestors (VIA nodes) are annotated with meta data indicating to whom they are possibly connected, and by which DNA cousins; b. the ICW-DC node is stored in the local and global shared attributes DB's; c. the ICW-DC nodes grown between two DNA matched User's VFT VIA nodes, will have additional information indicating the number of disembodied cousins either above or below in a path that DNA could have flowed, and this information will be used to enhance the strengths (weights) of the connections; d. this data will be displayed on the nodes info-display (1706), to help the User visualize how many ICW-A lead up or down to the particular node; e. and, for each of the ICW-A's contributing evidence, an ICW-DC node is grown between the ICW-A node of the User and each corresponding ICW-DC node in the cousin's VFT; f. and, to guide the MRCA-Engine with respect to the evidence of which node is the vertex of a fan-up or fan-down, an attribute node is grown from the presumptive vertex to each of the ICW-A nodes, with the type indicating whether it is a fan-up or fan-down case, how many VIA nodes are involved, and a weight proportional to the count of contributing ICW-A nodes; g. and, that these collections of ICW-DC nodes suggest that any MRCA between the two Users most likely is not above a fan-out up vertex, nor below a fan-out down vertex, because the assumption is made that the ‘disembodied cousins’, if they are not just statistical coincidences, represent cousin ancestors who are on a path that DNA flowed from an MRCA to one or the other DNA matched cousins, and if there are multiple paths above a vertex, then it is unlikely that all those cousin Ancestors provided the same DNA segment to a User, and if there are multiple paths leading down from a vertex, then it is unlikely that the MRCA is below the vertex, since the DNA mostly likely passed through the vertex heading down to the each of the cousins, and thus the vertex is the lowest likely MRCA, unless there happens to be a case of endogamy wherein cousins below the vertex produced offspring who may be the MRCA; h. and, when the MRCA engine stimulates a pair of MRCA-Vdna nodes, and those in turn stimulate their connected eligible VFT VIA nodes, an advantage will be given to the VIA nodes which connect to ICW-DC nodes, and to the Ancestors connected to the vertex nodes between clusters of disembodied cousins. 41. The system of claim 1, which is in part comprised of the sub-system (3500) represented in FIG. 35) as an illustration of one embodiment of Speculative Tree Search (STS) Agents attempting to connect nodes suspected to be related, wherein sub-system (3500) further comprises; a. a unique ability to automatically create speculative trees or connecting ancestors, and re-evaluate local DNA matching completeness, with the holistic support of constraints, fuzzy logic and various clustering systems; b. speculative Tree Search Agents build ‘what-if virtual sub-trees, when an MRCA can not be found between two DNA matched Users, but the search space has been narrowed down sufficiently to suggest that a particular branch in each tree should intersect, and wherein the objective is to find an ancestral path (DNA flow) between ancestors in two trees who may be separated by generations, with no known path between them, but who otherwise have strong hints that they have common ancestors, and whereas these hints may come from, as an example, a combination of DNA tree pruning, ICW-M and ICW-A clustering, disembodied cousin analysis, or an MRCA analysis that has left only a few branches as candidates but has found no direct link between two DNA matched Users, and whereas other ‘Expert’ knowledge may be coded in, such as the case of middle names often indicating the surname of some notable ancestor; c. and, given an DNA match between two Users' and a higher probability and resulting hypothesis that the MRCA is associated with a particular branch, then there are various strategies of ‘fill-in’, including up-ward exploration from a shallow tree and downward exploration from a deep tree, and wherein the search strategy and algorithms vary depending on modality which may include, for example, a breadth-first survey of a candidate ancestors' children, resulting in an ordering of the children candidates based on fit and constraint satisfaction, and for another example, choosing the best-fit child and descending depth-first, with again an ordering of the children at the next level down, wherein here, it is clear that the STS Agents make good use of the Constraints and Fuzzy-Logic DB and attributes on the Ancestor Nodes to determine fitness of candidate nodes; d. and, in general, the search progresses with two nodes, a top and bottom (X and Y and 3514), wherein each node must have certain attributes which suggest they may be related (ie, surname, DNA, location, or —the node is one of the few remaining options for a Vdna/VIA match); e. and given an Ancestor with K (count of) suspected children, each child is evaluated to see if it could lead down to the bottom node, wherein a first strategy comprises: if Surname is the common attribute between the bottom and top nodes, look at each male child, and then look at their locations, and sort according to which is closest in place and time, and then each child node is ‘explored’, in that if it has children, those are searched in the same manner; f. and, if the ancestor of interest does not have children in the VFT or VWT, an initial search is done of all DNA matches (starting with VFT's of User's in the ICW-Match list between the top and bottom node originators, and then progressing to all DNA-match VFT's of the top and bottom nodes) to see if a VFT has this node with children, and if so, they are then added to the exploratory tree (along with confidences), and explored, wherein adding a node means replicating the node's meta data, but with only the pointers (links) to the children, as we do not want to copy entire sub-trees when doing a search; g. and the search of VFT's, in the order prescribed (ICW-Matches between A at (3502) and B at (3504), all remaining DNA matches of A or B, then all remaining VFTs) for a particular ancestor should accumulate a list of all matching ancestors, and the data of all matching ancestors that passes a relevance criteria will be merged into one VIA node (representing that Ancestor), and will be analyzed by the constraints Agents and confidence Agents, and if passing quality criteria, may be added to the VWT, and furthermore, in this respect, a search for a given ancestor is not repeated multiple times for other cases involving that ancestor; h. and if the VFT and VWT scan is not successful in building a viable ancestor at a particular level, the node will be marked, or ‘bounded’ in the traditional sense of branch-and-bound, and the node, based on its current viability value, will be inserted into a list of other nodes pending for further evaluation, and in this respect, a breadth-first at level N, and depth-first search is enabled, wherein the viability criteria is initially high, thus this search will explore all paths until each falls below the current viability metric, and after this, if no solution is found, the viability watermark will be lowered, and the nodes in the list which are above that watermark will be again searched in the same manner, eventually finding a solution, or adding more nodes to the list, or reaching a dead-end (leaf) for all sub-trees; i. and after the VFT's and VWT are searched for existing nodes, a general genealogic sources search may be executed for any nodes in the pending list which have a viability metric still suggestive of their having a potential path to the target node; j. and after the search has completed, the new branch or branches are added to the VWT, and shared with the Agents of the requesting VFTs, and if no viable path is found, but there is still a ‘weak’ path with missing links, this will be added to the VWT as a virtual branch with virtual-ancestor placeholders at each generation, whereas the branch is annotated with information to record the cause of the search, and thus, if other searches are triggered based on similar DNA matching Users, then the evidence for the Virtual branch being the actual branch will increase, and furthermore, the MRCA nodes from the User's VFT's will also need this recorded, such that the same search is not repeated, and furthermore, if an alternate solution is found, the Virtual Branch annotations must be retracted, and wherein the this form of search is similar to the ‘Ant algorithms’, wherein the ants leave a pheromone on a path to food, and as more ants find the same food, the pheromone increases. 42. The system of claim 1, which is in part comprised of the sub-system (3600) represented in FIG. 36) as a flowchart of one embodiment of the Closest-Point-Of-Approach analysis of VFT's of DNA matched Users, which is also comprised of: a. the sub-system (3700) represented in FIG. 37), an illustration of an Ancestor Migration visualization tool with sliding time-windows, pedigree path traces, and proximity halos; b. an automated ability to discover mating eligible and likely ancestors residing in the family trees of DNA matched Users, based on proximity of co-location during the same reproductive time period, and use that data in automated MRCA analysis, and wherein from this analysis, attribute nodes will be created which represent this proximity in the MRCA Engine analysis, and furthermore, proximity analysis may be used to determine if a child and potential parent were in the same place-time ... preferably at date of birth; c. An algorithm for proximity discovery, as depicted in the flowchart of system (3600), consisting of: i) Migration Proximity Influences, a proximity analysis begins at state (3602): ii) for all eligible Ancestors between DNA Matched User A, B, and then; iii) (3604): create a matrix for CPA between each eligible pair, then; iv) (3606): evaluate the ICW Matrix to rank similarity of the candidate individuals (taking into account such constraints as age, gender, so as to not try to mate same-sex, or women before or after child-bearing age, and then; v) from this, we create (3608), an ordered list of pairs of Ancestors to test, of which each pair is passed to (3610) Proximity Search Agents; vi) then in state (3612), the Proximity Agents calculate the closest point of approach based on calculated birthdates and travel path timelines, wherein this is done intelligently by the Agent by walking the travels of the two ancestors from place and date of birth to place and date of death, and for each decade, the estimated distance between the two is used to calculate the smallest CPA between the two ancestors; vii) in state (3614) the results are saved to the Shared Attributes DB, and then; viii) in state (3616) a ICW-Proximity attribute node (ICW-P) between a pair of Ancestors may be saved to the Shared Attributes DB; ix) and finally, state (3618) registers the changes (new attributes) to the Agent Exchange to notify the calling system of proximal pairs of ancestors, wherein the calling system may be the User, in which case the attributes are graphically annotated. 43. The system of claim 1, which is in part comprised of the sub-system (3800) represented in FIG. 38) as an illustration of an In-Common-With Matches data-mining and processing system, in one embodiment, with cluster analysis enrichment improvements arising from said system (3800) of which comprises an automated system and methods to ‘data-mine and cluster’ in-common-with (ICW) matching members between two matching members, such as a 3rd member who matches both of a pair of matching members, and which in general makes the estimation that clusters of highly inter-connected Users (DNA cousins) may share a common ancestor or at least a commonality in some biographic data, such as the time and place that their common ancestors lived in, or the social groups those ancestors mingled in, and that this improvement on the ICW-Match clustering analysis leverages the various commonalties between the VIA nodes in the DNA cousins' to highlight those ancestors between the members of a cluster, who share a majority of common attributes, and which further comprises: a. the sub-system (3900) represented in FIG. 39) as an illustration of a method of using In-Common-With Matches along with good MRCA data to algorithmically reduce some MRCA search spaces, in one embodiment, wherein some ICW-Match sets which have cases of solved MRCA's between members of the match set, are clustered around those MRCA's, and DNA flow logic is used to determine, or predict, under which branches of the tree Users must lie, and that this system is primarily used to evaluate ICW-Match data, wherein the DNA segments are not known, but the fact that several User's DNA match each other is known, and wherein this system is also applicable to the case where the DNA segments shared between several Users is known to the system (but not necessarily known to the Users), and in this case, there is no ambiguity of which segments match (the S1, S2, S3 in FIG. 39), but the mapping of the segments to the VFT graphs follows the same fundamental pattern, wherein this analysis comprises: i) ICW-Match analysis, in one embodiment, will start with the closest relatives (participant Users who DNA match) of the User, who have already been tied to an MRCA, wherein any ICW-matches between the User and the first MRCA-triangulated cousin most likely will find their MRCA with the other two in the pedigree at or above that first MRCA . . . unless there happens to be a case of endogamy wherein cousin descendants of the 14 MRCA mated and one of them happens to be an ancestor of both the User and the cousin, and wherein in this case, the designated 2nd MRCA is a co-MRCA; ii) if a User has successfully populated their tree to great-grandparents, and have at least one DNA match confirming each of these great-grandparents, then they may be able to assign all DNA cousins who have ICW-Matches to them to one of the 8 branches of sub-pedigrees of the great-grandparents, and this process continues for all DNA cousins with known MRCA's; iii) the the case of 3 User's who form a triangle of DNA ICW-Matches (circled in 3912), forms the base case for the global population analysis of ICW-Match clustering, wherein this Global ICW-Match analysis is explained in FIG. 45), and in said FIG. 45), the ICW-M may be represented as in (3914), where S1 -S3 represent the DNA segments shared between the Users, and any one of the S1, S2, and S3 may be the same, or overlapping, segment, and whereas the fundamental theory of this system is that you must map the segments to the combined VFTs (or VWT), such that the DNA segments (S1 -53) of (3914) have a down-stream flow to their respective Users, and wherein two possible ‘network flows’ are illustrated in (3916) and (3918), and wherein the lines between nodes can represent multiple generations in a VFT, but the actual realistic distance these edges represent are bounded by the ‘Genetic Distance’ predictors for the DNA matches of the Users; iv) and wherein this restriction of the ICW-matches to the pedigree of the MRCA node is recorded by several means: (1) the MRCA-Vdna node of each ICW-Match updates its connections to the VIA nodes in the two VFT's to reduce the connection weight to nodes (ancestors) below the MRCA, as described in (3916), wherein this is facilitated by connecting MRCA nodes with ICW-Match nodes; (2) by the Genetic Distance, an ICW-match X of a DNA cousin Z to the User A which is pinned to an MRCA-AZ, can have its own MRCA-XA pin-pointed by calculating the ‘Genetic Distance’ from the DNA cousin Z, up to the MRCA-AZ, and then up and/or down to the ICW-Match X, and that this may be formulated as a constraint, that the MRCA for A to X must lie within K generations of MRCA-AZ, on any path up or down except down the path to A; (3) by creation of ‘ICW-M Cluster nodes’ to bind ancestors who share attributes across the ICW-Match sets, wherein cluster nodes may point to other cluster nodes to create a hierarchical cluster, and wherein the weights of the connections infer a form of connectionist fuzzy logic, and thus propagate constraints; (4) and by creation of ICW-A (common ancestors) nodes with ICW-Match enhancement, for example: an ICW-A node which connects to a ICW-M node, which itself connects to the MRCA's of involved Users, and/or connects to ICW-Match Cluster nodes; b. the sub-system (4300) represented in FIG. 43) as an illustration of one embodiment of an automated ICW-M Graphing System sub-system, with the description of ‘system (4300)’ included here in full, wherein each node represents a DNA cousin to who the User matches, and each bi-directional line indicates that the two connected DNA cousins also match each other, wherein it may be estimated that there is some relationship (by either DNA, social circles, location or other attracting force) that causes a cluster of DNA cousins to have a high degree of interconnectedness, and wherein in the display, any DNA cousin with who the User has a confirmed MRCA, will have extra emphasis on their node, such as the double-circle or donut-icon; c. the sub-system (4400) represented in FIG. 44) as an illustration of one embodiment of an ICW-M Graphing System, with the description of ‘system (4400)’ included here in full, wherein a typical mind-map of connections between Users who match each other as well as the first User, is expanded to include an intermediary ICW-DNA node between each pair of Users, such that the intermediary node represents and records the DNA segment(s) shared between the two connected Users, and the connection strengths are proportional to the amount of DNA shared; d. the sub-system (4500) represented in FIG. 45) as an illustration of one embodiment of an ICW-M Graphing System mapped to a VFT, with the description of ‘system (4500)’ included herein, wherein the basic objective of this system is to map each ICW-DNA node to a VIA node in the VFT of the User, wherein the possible choices for the ICW-DNA are constrained by conditions such as MRCA's assigned to various User nodes, and the genetic distance prediction between a first User (A)and the 2 nd User (B), and between both of them and the 3rd User(s) which formed the basis of the ICW-Match, and that any and all other constraints applicable, will be utilized and verified for constraint satisfaction, and wherein this information is passed to the ‘General N-ICW-Match Center of Gravity Algorithm’ (4512), and wherein when an MRCA is found, or predicted, between a pair of DNA matched Users, the ICW-DNA node shared between them in the ICW-M graph will be connected to each Users' respective VFT VIA Ancestor node representing the discovered MRCA, such that Ancestor nodes continuously accumulate putative DNA from MRCA match discoveries; e. the sub-system (4600) represented in FIG. 46) as an illustration of one ‘base triangular case’ algorithm embodiment of an ICW-M Graphing System with constraint-driven DNA mapping to several Virtual Family Trees, with the description of ‘system (4600)’ included by reference herein, wherein the genetic distance constraints, along with the DNA flows constraints, are combined to limit the group of ancestors that could be the MRCA between pairs of DNA matched cousins; f. the sub-system (4700) represented in FIG. 47) as an illustration of one embodiment of an ICW-M Graphing System with constraint-driven DNA mapping, with the description of ‘system (4700)’ included here in full. 44. The system of claim 1, which is in part comprised of the sub-system (4200) represented in FIG. 42) as an illustration of an Merged-MRCA browser, in one embodiment, whereas when MRCA-Vdna nodes are confirmed between two Users, they are linked together into a composite MRCA-Vdna Node, and this node may again be merged with by another DNA match, or may have already been a composite node, and thus, if a MRCA-Vdna Node is a composite, then in this graphical display, clicking on the composite node will display a star diagram of the individual MRCA-Vdna Nodes, with the User then able to click any one of those nodes to jump to the respective User's MRCA to VFT display. 45. The system of claim 1, which is in part comprised of the sub-system (4800) represented in FIG. 48), as an illustration of the embodiment of an ‘Combinatorial MRCA Assignment’ with constraint satisfaction metrics, wherein the system, given a set of DNA matched Users and their respective sets of VFT ancestors and corresponding MRCA's, shall ,as illustrated in FIG. 48) in general, select ancestors (Ki) from the sets X of eligible ancestors using one of the described algorithms in this claim, such that assigning MRCA (Mij) nodes to them results in an optimal assignment according to the objective functions of the algorithms used, and wherein the plurality of objective function metrics includes, but is not limited to: a. the cumulative measure of equivalence of the Ancestors chosen to be MRCAs, b. the satisfaction of constraints across all such assignments and their satisfaction rates on the VFTs and VWT, c. and the resulting total quality and completeness of the VFT's involved, and/or VWT; and which provides a unique ability to automatically apply constraint satisfaction algorithms to the mapping problem of a massive plurality of DNA cousins per user in combined sets of over a million each of DNA participants, using as constraints (for example) the holistic factors of confidence, DNA mappings or isolations, various data points, in order to highlight most likely branches for the MRCA between any pair of DNA matched Users, wherein in all cases eligible Ancestor nodes may be limited, diminished or enhanced (in their fitness within the respective objective functions) by the Constraint factors, which comprise: d. any DNA mapping between the members of the intersect set that is able to limit the eligible ancestor set between the members; e. any outright ICW-Ancestors in the respective pedigrees of the ICW-M set receive majority fitness valuations; f. surnames, or uncommon first or middle names which are similar to the Surnames of their potential Ancestors in other trees in the ICW-M set, are given priority and higher fitness valuations than attributes of less significance; g. CPA in time (closest passing in time), mapping all eligible Ancestors of the members of the ICW-M set simultaneously, via ICW-P attributes, should be met, if possible to calculate, wherein this is only impossible to calculate or estimate, if the there are no evidences of temporal location such as birth place, death place, or similar geo-temporal data points of the individuals parents, siblings or offspring; h. uncommon (statistically significant) Nationalities of birth, or ethnicities in Ancestors in the ICW-M VFTs; i. attributes (records) shared between any Ancestors in the ICW-M VFTs, such as Wills, names on marriage records, military service etc.; j. simultaneous Disembodied Cousin analysis from VFT Ancestors of the members of the ICW-Match set; k. cluster attractors, such as ICW-Match clusters, as tracked by ICW-DNA nodes, wherein attractors are limited by DNA match Genetic Distance estimates; l. ICW-Match DNA flows, such that DNA from a putative MRCA must flow downstream through the pedigree to the matching DNA individuals (Users), and wherein the sub-systems selection and objective function methods comprise the ‘Best-First’, ‘Evolutionary Algorithms’ and the ‘General N-Cluster Center-of-Gravity Algorithm’. 46. The sub-system of claim 44, comprising a sub-system method (4808) called ‘Best-First’, wherein the best MRCA candidate is chosen from the most cluster-enriched (fit) User pairs first, and all User's are run asynchronously, in parallel if possible, and such that the algorithm can operate on the VFT's directly, but can also run with the (608) Inter-Match Network, and that the detailed algorithm comprises the following steps: (1) all User MRCA-Vdna candidates (Mij) of a particular User ‘i’, are ordered (queued) by the likelihood of finding a common ancestor between the MRCA's candidate VIA nodes in sets Xi and Xj, where Xi is the set of VIA candidates from User Mi, and Xj are the candidates from User Mj, and where the MRCA node Mij is thus the MRCA between User ‘i’ and User ‘j’, and the ‘i’ index are pre-selected as DNA matches, and pre-sorted such that the Mij with the highest confidence (and presumably, closest DNA relationship to the User ‘i’) are processed first, and thus, the metric, ‘likelihood of finding a common ancestor’ is, in one embodiment, calculated by taking those sets X which have the fewest elements (fewest VIA nodes), and which already have the highest degree of shared attributes, and wherein the example function fcd(Mij) below, suffices to provide a simple ranking of all input MRCA candidates: a. fcd(Mij I, where function fcd calculates the ‘cluster density’ such that fcd(Mi, Mj)=Num_Shared_Attributes(Mi,Mj)*(1/(Tot_Num_Members_in Xi+Tot_Num_Members_in_Xj)), where this example function calculates a simple density, without regard to weighting of importance on the attributes; (2) from the set Xi of Mi selected, the most likely matching Ancestor for Mi's two Users is chosen; (3) thence, each next less fit MRCA pair that is related to the prior pair is evaluated, if any more exist, and any improvements in the network are taken into consideration (ie, the prior MRCA assignment reduces the eligible set for the next, related MRCA), and then, if no DNA related MRCA exists, the next best fit of the remaining MRCA's from the set M is chosen; (4) loop back to step (2), select an Xi of the last Mi; (5) repeat until all MRCA have been assigned; (6) after all MRCA have been assigned to the User's VFT VIA's in the first round, calculate the fitness of the total assignment, wherein this fitness is the sum of the fitness of each MRCA assignment, and any various global factors (overall quality and completeness of VFT and VWT trees resulting), and wherein the fitness of each MRCA assignment is a function of: a. the confidence in the match of Ancestors selected for the MRCA, according to the ICW-A search Agent algorithms; b. the satisfaction of the Genetic Distance function for the MRCA, with the two selected Ancestors to each respective root User node, wherein any deviation is a negative addition; c. when two or more MRCA's are assigned to the same VIA node, then the MRCA's have to be partitioned into sets according to unique VIA individuals, wherein, if the VIA from the other VFTs nodes do not match each other as ICW-A equivalent individuals, then they must be partitioned into sets of individuals who do match each other, and wherein the total fitness that could be assigned to any one MRCA is shared between the sets of MRCA-VIA partitions, with fitness weight apportioned according to proportional numbers of VIA nodes in each set, wherein, if set 1 has 3 VIAs, and set 2 has 2, then Set 1 MRCA nodes would share ⅗ of the fitness; (7) next, the worst performing MRCA assignments (eg, those that perform below acceptable criteria for a valid match), are evaluated to see if any other assignment would have performed better, and the new assignments are not yet made permanent, but are rather put in an evaluation bin for each MRCA, and the new assignment is marked, to prevent it from being ‘re-evaluated’ again in this current round, and: a. if the re-assignment disrupts a prior assignment, then that prior assignment is re-visited, wherein if every prior assignment had already been optimally selected, then the worst performer has been optimally selected from the choices it had, and thus, to make an improvement (if possible), would require a disruption of a prior assignment; b. the disrupted assignments are queued and re-evaluated by looping back to step (7); c. The re-evaluations continue until the queue is empty, or until there are no further options for re-assignment, as all options have been marked in the current round, (8) after the current re-assignment round is completed, the whole re-assignment set is calculated for overall fitness, per the measure of step (6); (9) if the measure of overall fitness has improved, the evaluation selections are made primary for each affected MRCA node; (10) step (7) re-evaluation is run again, and the results measured again, and compared against the prior run, until there are no further improvements in the overall fitness. 47. The sub-system of claim 44, comprising a sub-system method (4810) called ‘Evolutionary Algorithms’, which consists of ‘Smart Genetic Algorithms’, wherein the system will create sample sets from the best performances of each MRCA, and the method may be run on individual VFT's, but can also run all VFT's in parallel with the (608) Inter-Match Network, which thus facilitates global constraint satisfaction and optimization, and, A traditional Genetic Algorithm (GA) implementation requires the selected set (assignments of a User's MRCA-Vdna nodes to eligible Ancestors) to be ordered into a vector, with a population of such vectors representing various assignment sets, wherein the order of MRCA's on every vector must be the same, and wherein an initial assignment may include the (4808) Best First, and then vectors generated from randomization of the less optimal assignments, and rounded out with a number of more randomly arranged assignments, to avoid what's called the ‘minimal deception problem’, and wherein after a population is created, the optimization process applies an objective function to each vector to determine the fitness of each, and wherein a number of the highest fitness vectors are chosen for mating, and wherein, in the traditional GA mode, iterative cross-over recombination is done with such vectors to generate new offspring (samples), and wherein this process is repeated until there is no significant improvement in fitness of the best performing vector, and wherein that vector is then re-evaluated to confirm constraints, and then those assignments are given to the VFT and VWT Agents, and wherein it is noted that, in this system, each column (when vectors are aligned in rows, the column represents a particular MRCA), will have a population of potential Ancestors which may fall into and particular row's assignment of that MRCA, and wherein once an Ancestor gets dropped from the population represented in a column, it can not be added back in by this system, and wherein this limitation leads to the Smart GA, wherein The traditional GA is one embodiment of this algorithm, and the preferred embodiment is called a ‘Smart Genetic Algorithm, and whereas this system will create sample sets from the best performances of each MRCA, and this method may be run on individual VFT's, but running all VFT's in parallel with the (608) Inter-Match Network, facilitates global constraint satisfaction and optimization, and whereas this process is comprised of the the following flow: (1) create a large set of constraint satisfactory assignments of Ancestors in a VFT to a User’ MRCA-Vdna Nodes, say K, (number of sets depends on memory and compute time available, but should be high enough that every permutation of assignments for each MRCA is expressed enough times to ensure that its correct assignment shows up enough times, with the correct assignments of those adjacent), with each saved as a vector of tuples, which consists of an MRCA id, two VFT-VIA' s ids, and the fitness of the VIA assignments, wherein this is initially accomplished by: (a) randomly select one MRCA-Vdna, randomly select one Xi for each Mi, then calculate the local fitness of the assignment and save it on the vector ‘tuple’ for the Mi'th node; (b) the ‘fitness’ of an assignment involves, in one embodiment, a summed metric of (i) the DNA match confidence and degree; (ii) the matching of the VIA members of an MRCA assignment, which includes, at least: 1. biographic information (name, date-of-birth, parents, siblings) 2. physical location overlap 3. other attributes shared (through co-connection to the same attribute nodes); (iii)constraints satisfaction quality, wherein negative additional fitness may be accomplished by cases of Genetic Distance violation, or non-convergent DNA flows (a DNA segment does not have a common ancestor, but rather two or more distinct Ancestor paths which do not intersect); (iv) the quality of the VFT's with the Ancestor involved in the MRCA assignment, wherein, equating two Ancestors from two or more VFT's, means that each VFT must determine whether the information associated to that Ancestor in the other VFT(s) actually improves or diminishes its' own quality, and where it must also allow for the possibility, if there are many members of a triangulated MRCA, and there is a definite fit of this MRCA into the User's tree, but the Ancestors do not match or do not match exactly, that its' own instance of the Ancestor is wrong, wherein, if the parents, siblings or descendants match, but the actual current Ancestor at the node does not, then that Ancestor should come under scrutiny; (c) repeat la until all MRCA's have been assigned, then calculate the overall fitness for the whole assignment set (which is recorded in the header of the vector of tuples); (d) calculation of the overall assignment is a form of the Quadratic Assignment Problem, wherein the fitness is based on the summing of the individual assignment's fitness; (2) from the set of assignment vectors, sort and rank them according to their overall fitness values, wherein we note, a vector in this case is the assignments for a single User with his/her MRCA cases assigned to his/her VFT VIA's; (3) if the best performing assignment has successfully assigned every MRCA with high (acceptable) fitness, make that assignment permanent in the MRCA's and stop; (4) if the best performing assignment is unsatisfactory, proceed with a ‘smart reshuffle’, which is similar to cross-over but is not blind, wherein a reshuffle consists of: (a) sort each vector according to the fitness's of the MRCA assignments it holds, such that performance decreases down the vector; (i) during the sort, create a hash-table of the vector, with the MRCA id's as keys, and a pointer to the vector index as value, for fast lookup; (b) for each MRCA Mi, find the N best assignment's fitness from L vectors out of all of the top performing of the overall K vectors, then copy each to N=K-L new vectors, such that: (i) this will result in a new population of Assignment vectors, sized N+L, based on the best performing individual MRCA assignments and overall performances; (ii) individual MRCA assignments are like real genes, in that they compete in the environment (fitness calculation); (iii) the overall vectors of assignments are like individuals, in that they may have flaws, and those flaws limit their fitness; (iv) the recombination described above is able to pick the best MRCA assignments from all vectors, rather than just pair-wise as is done in 2-sex reproduction; (5) merge the L best overall assignment vectors and the new N vectors, resulting in a new population of size K again: (a) Calculate the overall fitness of the new vectors; (6) if there has been some improvement in the fitness value of the best performing vector, return to step 3, such that (a) if the there is a good solution and no further improvement seen, stop, otherwise it will repeat the process; (7) if the last round (generation) did not result in significant improvement, and the overall fitness is below expectation, the system will have to focus on sub-optimal nodes: (a) sub-optimal nodes are found by finding and date-mining the worst performing MRCA assignments in the best performing overall vectors; (b) any MRCA assignment which consistently shows up in the top performing vectors, but is itself sub-optimal, should be re-sampled; (c) regenerate these MRCA assignment by either: (i) using the most fit MRCA assignments from all samples, regardless of overall vector fitness; (ii) regenerating the MRCA's assignment of Xi by trying other nodes from the eligible set X, which have not been tried before; (d) after regenerating the worst-performing MRCA assignments, loop back to step 4; (8) if there is no improvement after a number of ‘Sub-optimal’ node re-shufflings, the system will have to look for ‘conflict nodes’: (a) conflict nodes are MRCA assignments that result in conflict with other MRCA assignment of the same vector set, and wherein there are various manifestations of conflicts; (b) if an Xi assigned to an MRCA (and thus, calculated to be the same individual as Xj) also appears in another MRCA assignment, but the second MRCA has it paired with an individual Xk who does not match Xi, then this is probably a conflict; (c) if the MRCA assignment leads to a case where DNA cannot flow downstream to satisfy all MRCA assignments, then it is in conflict; (i) testing for DNA flow consistency requires a build of the representative trees using the VFT's as the framework; (ii) with 1000's of MRCA's per User, there will likely be several MRCA's associated to every VFT VIA node (Ancestor); (iii) on the affected VFTs, each MRCA is applied, and a DNA packet is sent down from the MRCA to the User root nodes; (iv) following the theory of FIG. 46) and FIG. 47), if 3 or more Users are DNA matched, and there is no direct downstream flow for DNA to all of them, then at least one of the MRCA assignments is in conflict, whereas, usually, if a majority of them have a direct DNA path to all DNA matched Users, then the minority MRCA's will be marked as conflict, and will be recycled; (d) if any conflict nodes are found, they will be marked for recycling (or reassignment), and the procedure will loop back to step. 48. The sub-system of claim 44, comprising a sub-system method, (4812) called ‘General N-Cluster Center-of-Gravity Algorithm’ wherein the ‘General N-ICW-M Center-of-Gravity Algorithm’ is applied to sets of ICW-Matches who share various attributes which cluster them around a particular region of a graph, and wherein, given that the VFT's have been data-mined for common attributes, ancestors and DNA, and that those have been registered in the Global Shared Attributes DB as Clusters (for example, a set of ICW-M networks (4404) for each User), then the objective of this algorithm is to engineer an attraction between members of a Cluster or ICW-Match network and their shared, dominant cluster attributes, which thus attracts them to in-common ancestors or ancestor groups, and wherein the system will provide negative pressure to enable separation of sets with common-centroid accumulations, and wherein this algorithm is essentially the same as the Local MRCA Engine FIGS. 30-32), but with many sets of many MRCA's applied simultaneously, and wherein, in terms of the similar k-means clustering, we are trying to partition the DNA of all Users involved (the ‘observations’) to ‘k’ specific Ancestors (VIA nodes) or Ancestor Clusters, and wherein there is no simple distance metric by which to calculate the distance of a DNA segment to each cluster center, but there is, of course, no direct physical relation between the DNA code itself and clusters, and there is, however, a number of attributes we can associate to the DNA (the pedigree), and likewise to the Ancestors, and wherein it is noted that there will be many descendants of most ancestors, and therefore many DNA segments, and wherein although the attributes associated to a DNA segment may rapidly diverge over time (going down the descendant branches), they will almost always have overlap at the point of inception—if attributes related to that period have been discovered and recorded, and wherein if any particular DNA segment is attribute-poor in any region between the descendant and MRCA source, then this system can still work if there are sufficient ICW-Matches through which the descendant's DNA segment can be pulled into a cluster, and therefore, to calculate the distance of a DNA segment to any particular Ancestor or Cluster centroid, we need to quantify the value of the attributes, and their confidences, between the DNA and Ancestor, and whereas, unlike K-means, we may also employ various constraints to help sort the DNA into these clusters (such as Genetic Distance and direct downward spanning-tree DNA flow from the ancestors to Users, for all solutions), wherein we will always want to utilize any DNA mapping to associated to DNA cousin networks, and ICW-Match networks to ‘inherit’ attribute influences, wherein this algorithm consists of: (1) give each Cluster and/or ICW-Match network a name (tag), which will be sent with packets, then, the MRCA's involved are derived from the Cluster and/or ICW-match network; (2) fire activation through all relevant MRCA's of all Users in a particular named network, with the name tag, and DNA ID, wherein we note that these activations go to nodes which have been pre-pruned to only include Ancestors who are within the Genetic Distance range; (3) activation spreads through the network in the same manner as described for the Local MRCA Engine, FIGS. 30-32), wherein we note that activations are travelling through distinct VFT's, and attempting to find where those VFT's intersect, given the evidence of the DNA match; (4) the activations received at each Ancestor are summed by source (DNA ID), wherein these values serve as the corollary of K-mean's distance metric; (5) the Ancestor nodes of a VFT are scanned to make a table (DNA-per-Ancestor), VIA nodes on rows, DNA ID's as columns, with row-column values as a ‘tuple’ of the activation received from a DNA ID, the ID, and the network/cluster name tag, wherein we note that the DNA ID may end up at several ancestors, and wherein this format enables us to sum up the number of occurrences of a DNA ID from a particular network or differing networks, and differing MRCA origins; (6) another table (Ancestors-per-DNA) is simultaneously built, with DNA ID's as the rows, and Ancestor ID as columns, wherein each Ancestor receiving a DNA-ID packet will record that packet value in the row of the DNA ‘ID, and wherein this basically enumerates the ranking of where a DNA segment predominantly ends up; (7) the tables are analyzed, wherein a DNA ID may have the its highest value at a particular Ancestor (Ancestors-per-DNA), while that Ancestor may have other DNA ID's as having higher frequency in DNA-per-Ancestor (total activation), and wherein generally, we want to find DNA segments originating from different sources to a particular Ancestor, and that at least implies the Ancestor is the MRCA or downstream from the MRCA, and then ancestors receiving multiple sources of the same DNA are evaluated and ordered, such that the oldest (further back in time), is considered the earliest possible known MRCA source; (8) with these tables, further complex analysis will be possible, and may be merited, taking into account ICW-Match relationships of DNA ID's, and applying the algorithms of FIG. (46) and FIG. 47); (9) the output of the analysis will be an assignment of the MRCA to particular Ancestor nodes with confidence derived from the above analysis. 49. The system of claim 1, which is in part comprised of the sub-system (5000) ‘Global DNA Cluster Generation and Analysis with Competitive Neural Networks’ as represented in FIG. 50), where sub-system (5000) comprises: a. a paradigm of neuromorphic inspired dynamic DNA-centric cluster generation, with spontaneous growth of correlation nodes between co-activating nodes, and decay of nodes which have lost co-activation, and; b. a system to coalescence overlapping DNA into new ‘overlap’ or ‘merged’ DNA nodes, and; c. a system of ‘floating’ DNA segments shared between two or more Users, wherein floating means an MRCA has not been found for the shared DNA segment, and such that they are associated to eligible nodes by pointers thus creating a cluster, and; d. a hierarchical system of DNA clusters wherein a ‘Cell’ node is the vector through which DNA must pass, and; e. a system of ‘Trait’ nodes which represent the best-known phenotype of DNA SNP's, which bind to DNA segment nodes, their Cells, and potentially to VIA nodes if a VIA is known or hypothesized to harbor the Trait, and; f. a means of simulating the ICW-DNA network by the MRCA-Engine FIGS. 23, 24, 30, 31, 32), with several variations described below, and whereas the MRCA-Vdna nodes send DNA packets to all eligible VFT VIA's, which then relay them to all connected Attribute Nodes, Trait nodes, and Cell ICW-DNA nodes, which then relay to all connected Segment ICW-DNA nodes, and wherein the relayed stimulus packets contain their ID's, and paths traveled, and the Genetic Distance range expected to the User, and the activation level of each packet is modulated according to the strength of each connection traversed; g. a plurality of competitive neural network (CNN) analysis modes being comprised of at least two modes of highlighting the most associated ancestors between trees which include a ‘Burst Mode’ and ‘Evolving Mode’, wherein these example modes comprise: i) a ‘Burst Mode’ which relies on one burst of activations being sent out and then settling (decaying), until the winners are left, and wherein every DNA segment (from MRCA nodes and Chromosome DB's associated to Ancestor nodes and ICW-DNA) is activated simultaneously, and all VFT's are represented in the competitive neural network (through the 608) Inter-match DB), and wherein, given that activation packets carry the ID of the DNA segments or Cells from which it originated, and given amplification at nodes which receive multiple activations from the same DNA ID originating from different trees, and given a decay rate of the activations to ensure limited growth and eventual decay, and given further decay on nodes which have competing multiple DNA ID activations for the same chromosome map location, with negative activation sent back on the losing DNA ID paths, and given a similar competition solution for each DNA ID (Segment) which is on multiple VIA nodes which are not in a direct line of inheritance, such that the top Node (the DNA node on the VIA which has the greatest activation) gains activation while the others decay proportionally, the entire system will be made to ‘settle’ such that each DNA ID should end up with one progenitor Ancestor (or couple), and that DNA ID should only appear in direct downstream paths from the progenitor(s), and each Ancestor will have no more than two DNA representations for any particular span on its' chromosome map, and the progenitor(s) of the segment will have a Genetic Distance to each User having this segment, which is within the estimated range, and wherein: a VIA node will reject (ignore) a DNA packet which has a Genetic Distance range, which is greater or less than the VIA node's Genetic Distance to the VFT root node, and wherein once such a DNA ID has settled to one progenitor Ancestor, a direct connection is grown to that ancestor between the ICW-DNA segment node and the VFT VIA Ancestor node, and the condition is reported to the MRCA-Vdna node, such that it may register this ‘solution’ for this particular algorithm, and wherein the side-effect of growing the connection from the DNA-Segment node to the Ancestor(s), affects other algorithms that depend on activation passing through attribute nodes connect to each VIA Ancestor, and; ii) an ‘Evolving Mode’ in which an average of a rate of activation received is used to determine dominance, wherein the MRCA-Vdna nodes send out activation packets every time there is an addition or change to the ICW-DNA nodes or attribute nodes, or whenever a settling time has passed, and wherein the entire system is continuously (on a periodic beat) sending packets from MRCA-Vdna nodes, and wherein in this mode, the system dynamically accommodates all constraints from all VFT's and all DNA matches in a simultaneous, evolving solution, and wherein the conditions described in the Burst Mode are honored in this mode as well, as well as the resulting actions of connections growth from a dominant DNA Segment Node to VIA node due to activation association, and furthermore, the type of simulation mode (Burst or Evolution) is encoded into, and sent with each packet, such that both may run overlapping, and nodes will not get confused, and wherein each node will have registers (variables) which account for Burst and Evolution mode packets received and passed, and wherein evolution mode does not require the nodes to be uploaded to the (608) Inter-match DB, but rather, has direct peer-to-peer communication between the User's MRCA nodes, VFT nodes, attribute nodes and ICW nodes, and wherein this peer-to-peer communication is mediated through the Agent Exchanges, and various Agents, and if two nodes which are exchanging a packet of activation information lie on different computers, then Agents will have been initiated on each of those computers, and wherein the Agents communicate by various message passing protocols, which may include TCP or UDP, and wherein the User Datagram Protocol is preferable in Evolutionary mode, as reliability is not critical as it would be in Burst mode, and wherein, in the ‘Evolutionary’ mode, a node determines which packets are dominant by calculating a frequency metric, wherein a node may receive multiple packets of the same type, or originating from the same Ancestor, or the same Cluster, and where, for each path from a first User A to a DNA matched second User B, passing through Attributes they share, there should be one packet of activation shared, and wherein the higher frequency attributes from a first Ancestor ‘wins’ in terms of dominance, over the attributes from another second Ancestor, wherein the metric for an attribute is an average rate, and whereas, in the burst mode, the metric will be a simple summation for the cycle, and furthermore, a ‘wins’ means that, if there is a consistent, repeated activation association between two Ancestors, then a direct ICW-A node will be grown between them, in the neuromorphic sense, and furthermore, this ICW-A node may increase its weights of connections, or decrease them, by rate of activations passing between the two nodes, such that, every ICW-A connection in this modality will have a small decay rate, such that if any Ancestor connected to does not co-activate with other Ancestors connected, then it can be assumed that the Ancestor has lost the shared attributes which motivated the creation of the ICW-A connection in the first place, and it shall be allowed to decay away, and; h. an ability to cluster, or associate phenotypes to genotypes, as described, comprising: i) a plurality of ICW-Cell nodes, wherein a ‘Cell’ represents a collection of chromosomes and DNA, and wherein each such node connects to a VFT node, and connects to a plurality of ICW-DNA segment nodes, and connects to a plurality of Trait nodes, wherein the Trait nodes point to a DNA segment node and that the Trait represent the putative phenotype of the DNA segment; i. a plurality of ICW-DNA phased nodes, where a phased node is a construction of DNA in the popular mean of phasing DNA from known relatives, and j. an ability to recreate, in part, an ancestor's phenotype from accumulated DNA on an MRCA node, and the traits correlated to that DNA, as described in various public-domain SNP catalogs; k. an ability to discover which DNA sequences lead to resistance (Traits) to various diseases and conditions, by correlating survival and morbidity of a population (cohort) to DNA, as might be motivated in the event of, for example, a world-wide pandemic. 50. The system (100) and it's Agent-based sub-systems and Competitive Neural Network sub-systems, which in consideration of the holistic interaction of Agents, nodes, competitive neural network (CNN), constraints and evolving fuzzy logic, defines a general form of adaptive cognitive computing based on distributed networked computing systems with mobile Agents mediating activation between nodes proportional to connection weights, and, wherein said activations are transported as packets of information describing the type of packet, the path the packet (carried by an Agent) has traveled, and the distance the packet has traveled in terms of hops, and said Agents may carry with them fuzzy logic coded functions which may affect their actions at any nodes, according to their own state and the state of the node visited, and the states of other Agents presently at that node, which together form inputs to the fuzzy logic functions, and wherein that fuzzy logic may have outputs comprised of one or more of the following: a. if a visited node is the destination node, then the Agent will register itself with that node, leaving its state and travel history, and thence terminate itself, and such that the visited node will have accumulated the registrations of all Agents that have visited it (since the last reset); b. if a visited node has only one connection, that being the connection the Agent came in on, then said Agent may register with the node the fact that it has visited, leaving its identification, type and state, and thence terminate itself, as it has reached a dead-end; c. if a visited node has a plurality of connections, and the visiting Agent discovers that it (or a copy of itself) has already visited the node, it will terminate itself, as this represents a loop condition; d. if a visited node has only two connections, one being the connection the Agent came in on, then said Agent may register with the node the fact that it has visited, leaving its identification, type and state, and thence continue onwards down the next connection to the next node; e. if a visited node has a plurality of connections, one being the connection the Agent came in on, then said Agent may register with the node the fact that it has visited, leaving its identification, type and state, and thence replicate itself with one copy each continuing onwards down the next connection to each of the next nodes; f. in the above conditions, if an Agent also carries with it certain constraints, its actions may be controlled by the fuzzy logic it carries, such that, for example, if the Agent represents a DNA segment, and must only flow downstream (from Ancestor to Descendants), then if it is traversing a VFT or VWT, it will thusly only propagate itself (or copies of itself) down connections which satisfy said constraints, that being the children of the node it is currently on, and such that, for another example, if an Agent is exploring paths for an ICW-Match analysis, it may have with it a maximum generation (hops) counter as determined by the estimated Genetic Distance between two Users, and may deduct one from the counter after each hop, and terminate or stop after its counter depletes; . . . and wherein Agents may, according to their type and intent, initiate growth or decay of connections or growth or decay of connection strengths, such as when an Agent representing a particular origination entity, travels from one VIA node through the network to another VIA node, and there is evidence on that receiving node that the entity has been there previously, and the activation from that entity accumulated surpasses a threshold, and given this action the Agent thus reinforces the connection, or creates a shortcut, . . . and wherein Agents may, according to their type and intent, initiate growth of a new node and connections, such as when an Agent representing a Trait or DNA segment, travels from one VIA node through multiple hops through the network to another VIA node, and there is evidence on that receiving node that the DNA or Trait has been there previously, and the activation accumulated surpasses a threshold, and thus the Agent creates a shortcut, and wherein the Agents may carry with them an ‘activation’ packet, and the value of said activation may decrease (decay) after each hop, and may likewise be amplified at a node which satisfies some constraint on the Agent, such as a constraint that total activation originating from a source and accumulating at a node must surpass a threshold, and wherein the nature of an algorithm requires Agents to compete in certain cases, such that (for example), if a receiving node collects several Agents, but can only let one win, then it may enhance the result of the most ‘strong’ Agent (which may be according to the activation the Agent arrived with), while simultaneously sending the losing Agents home with an instruction to decrease the connection weights of the paths taken by those Agents.