Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented webinterface production and deployment system, for use with a source of user attribute data, comprising: a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying respective values for a plurality of hyperparameters of the candidate individual, the hyperparameters describing topology of a respective neural network and coefficients for interconnects of the respective neural network, each of the neural networks mapping user characteristics to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the dimensions for which values can be assigned; a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel; and a presentation module which: determines from the neural network described by a selected one of the candidate individuals, funnel dimension values identified by the neural network in dependence upon user attribute data from the source of user attribute data, applies the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values, and presents toward a user a funnel having the determined frontend element values. 2. The system of claim 1, wherein one of the available values for each of the dimensions is a default value, wherein the funnel has a default frontend element value for the default dimension value of each of the dimensions, and wherein the presentation module applies the frontend element values corresponding to all dimension values identified by the neural network other than the default dimension value for each of the dimensions. 3. A computer-implemented deployment system for a customizable webinterface funnel, the funnel being customizable along a plurality of dimensions, comprising: a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel; a neural network specification memory storing, for a particular variation of a neural network, a value for each of a plurality of hyperparameters describing topology of the neural network and coefficients for interconnects of the neural network, the neural networks mapping user attributes to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the dimensions for which values can be assigned; and a presentation module which: determines from the neural network described by the values stored in the neural network customization specification memory, funnel dimension values identified by the neural network in dependence upon user attribute data, applies the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values, and presents toward a user a funnel having the determined frontend element values. 4. The system of claim 3, wherein one of the available values for each of the dimensions is a default value, wherein the funnel has a default frontend element value for the default value of each of the funnel dimensions, and wherein the funnel presented toward the user by the presentation module has frontend element values indicated by the presentation database as corresponding to all of the funnel dimension values identified by the neural network other than the default dimension value for each of the funnel dimensions. 5. The system of claim 3, further configured to update the neural network customization specification memory with a new set of values for each of the hyperparameters. 6. The system of claim 3, wherein the system retrieves the values for the neural network customization specification memory in response to user behavior for entering a funnel. 7. The system of claim 6, wherein the system also retrieves the presentation database in response to user behavior. 8. A computer-implemented webinterface production and deployment system, comprising: a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying respective values for a plurality of hyperparameters of the candidate individual, the hyperparameters describing topology of a respective neural network and coefficients for interconnects of the respective neural network, each of the neural networks mapping user attributes to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the funnel dimensions for which values can be assigned; and network server infrastructure which, in response to one or more first requests received over a network, returns toward a first client computer system: a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel, a set of the neural network hyperparameter values identified by a first selected one of the candidate individuals in the population, and a presentation module which: determines from the neural network described by the neural network hyperparameter values, funnel dimension values identified by the neural network in dependence upon user attribute data, applies the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values, and presents toward a user a funnel having the determined frontend element values. 9. The system of claim 8, wherein one of the available values for each of the funnel dimensions is a default value, wherein the funnel has a default frontend element value for the default value of each of the funnel dimensions, and wherein the presentation module applies the first selected candidate individual to the presentation database to determine frontend element values corresponding to all funnel dimension values identified by the neural network other than the default dimension value for each of the funnel dimensions. 10. The system of claim 8, wherein the network server infrastructure, in response to one or more second requests received over a network, returns toward a second client computer system: the presentation database; the presentation module; and the set of neural network hyperparameter values identified by a second selected one of the candidate individuals in the population. 11. The system of claim 8, wherein a webinterface represents a webpage. 12. The system of claim 8, wherein a webinterface represents a mobile application. 13. The system of claim 8, wherein a webinterface represents an e-mail. 14. The system of claim 8, wherein a webinterface represents a social media message. 15. The system of claim 8, wherein a webinterface represents a social media feed item. 16. The system of claim 8, wherein a webinterface represents a chat message. 17. The system of claim 8, wherein the population of candidate individuals in the database comprises candidate individuals being tested for their success in promoting predetermined target user behavior, the system further comprising: a population initialization module which writes a preliminary pool of candidate individuals into the candidate individual population; a candidate testing module that tests each of the candidate individuals in the candidate individual population by live online evaluation, the candidate testing module developing a performance measure for each of the candidate individuals undergoing live online evaluation, the performance measure being an indicator of success of the neural network described by the hyperparameters of the candidate individual in achieving the target user behavior; a procreation module which adds to the candidate individual population new candidate individuals formed in dependence upon a respective set of one or more parent candidate individuals from the candidate individual population and the parents' performance measures; and a control module that iterates the candidate testing module and the procreation module, wherein the candidate testing module is included in the network server infrastructure and selects from the candidate individual population the selected candidate individuals whose neural network hyperparameters are to be returned toward client computer systems in response to the requests. 18. The system of claim 17, wherein the target user behavior is conversion by users. 19. The system of claim 17, wherein the performance measure developed by the candidate testing module favors increased conversion rates achieved in the live online evaluation of the candidate individuals. 20. The system of claim 17, wherein the performance measure developed by the candidate testing module favors increased revenue generated in the live online evaluation of the candidate individuals undergoing the live online evaluation. 21. The system of claim 8, wherein the network server infrastructure comprises: a CDN which, in response to a request from the client computer system, returns the presentation database and the presentation module toward the client computer system; and a conversion system which retrieves the first selected candidate individual from the database and, in response to a request from the client computer system, returns toward the client computer system the set of neural network hyperparameter values identified by the first selected candidate individual. 22. A computer-implemented webinterface production and deployment system, comprising: a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying respective values for a plurality of hyperparameters of the candidate individual, the hyperparameters describing topology of a respective neural network and coefficients for interconnects of the respective neural network, each of the neural networks mapping user attributes to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the funnel dimensions for which values can be assigned; and a machine learned content delivery network having accessibly thereto a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel, the machine learned content delivery network being configured to, in response to a request from a client computer system: retrieve from the database a selected one of the candidate individuals in the population, determine from the neural network described by the retrieved candidate individual, funnel dimension values identified by the neural network in dependence upon user attribute data, apply the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values, and deliver toward the client computer system a funnel having frontend element values indicated by the dimension values of the selected candidate individual. 23. The system of claim 22, wherein the machine learned content delivery network prepares the funnel in response to the request from a client computer system. 24. The system of claim 22, further comprising a conversion system having access to the database, wherein the machine learned content delivery network, in retrieving the selected candidate individual, is configured further to request a candidate individual from the conversion system, and wherein the conversion system is configured to select the selected candidate individual in response to a request from the machine learned content delivery network, and to return the selected candidate individual toward the machine learned content delivery network. 25. The system of claim 24, wherein the population of candidate individuals in the database comprises candidate individuals being tested for their success in promoting predetermined target user behavior, and wherein the conversion system comprises: a population initialization module which writes a preliminary pool of candidate individuals into the candidate individual population; a candidate testing module that tests each of the candidate individuals in the candidate individual population by live online evaluation, the candidate testing module developing a performance measure for each of the candidate individuals undergoing live online evaluation, the performance measure being an indicator of success of the neural network described by the candidate individual in achieving the target user behavior; a procreation module which adds to the candidate individual population new candidate individuals formed in dependence upon a respective set of one or more parent candidate individuals from the candidate individual population and the parents' performance measures; and a control module that iterates the candidate testing module and the procreation module, wherein in selecting the selected candidate individual, the candidate testing module selects the selected candidate individual from the candidate individual population. 26. A computer-implemented webinterface production and deployment method, for use with a source of user attribute data, comprising: providing a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying respective values for a plurality of hyperparameters of the candidate individual, the hyperparameters describing topology of a respective neural network and coefficients for interconnects of the respective neural network, each of the neural networks mapping user characteristics to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the dimensions for which values can be assigned; providing a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel; determining from the neural network described by a selected one of the candidate individuals, funnel dimension values identified by the neural network in dependence upon user attribute data from the source of user attribute data, applying the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values; and presenting toward a user a funnel having the determined frontend element values. 27. A computer-implemented deployment method for a customizable webinterface funnel, the funnel being customizable along a plurality of dimensions, comprising: providing a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel; providing, for a particular variation of a neural network, a value for each of a plurality of hyperparameters describing topology of the neural network and coefficients for interconnects of the neural network, the neural networks mapping user attributes to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the dimensions for which values can be assigned; determining from the neural network described by the values stored in the neural network customization specification memory, funnel dimension values identified by the neural network in dependence upon user attribute data, applying the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values; and presenting toward a user a funnel having the determined frontend element values. 28. A computer-implemented webinterface production and deployment method, comprising: providing a memory storing a candidate database having a population of candidate individuals, each of the candidate individuals identifying respective values for a plurality of hyperparameters of the candidate individual, the hyperparameters describing topology of a respective neural network and coefficients for interconnects of the respective neural network, each of the neural networks mapping user attributes to a respective funnel having one or more webinterfaces, each funnel having a plurality of funnel dimensions for which values can be assigned by the neural network, and the neural network identifying a value for each of the funnel dimensions for which values can be assigned; and providing network server infrastructure which, in response to one or more first requests received over a network, returns toward a first client computer system: a presentation database which identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel, a set of the neural network hyperparameter values identified by a first selected one of the candidate individuals in the population, and a presentation module which: determines from the neural network described by the neural network hyperparameter values, funnel dimension values identified by the neural network in dependence upon user attribute data, applies the identified funnel dimension values to the presentation database to determine frontend element values corresponding to the identified funnel dimension values, and presents toward a user a funnel having the determined frontend element values.