Finally LSBN employs an efficient algorithm , to merge structures of overlapping communities into a whole . In comparison with other four state-of-art large-scale network structure learning algorithms such as ARACNE , PC , Greedy Search and MMHC , LSBN shows comparable results in five common benchmark datasets , evaluated by precision , recall and f-score . What 's more , LSBN makes it possible to learn large-scale Bayesian structure by Model Averaging which used to be intractable . However , the relaxation can only be applied to two-class cases . In this paper , we propose full eigenvector analysis of p-Laplacian and obtain a natural global embedding for multi-class clustering problems , instead of using greedy search strategy implemented by previous researchers . An efficient gradient descend optimization approach is introduced to obtain the p-Laplacian embedding space , which is guaranteed to converge to feasible local solutions . Even under the worst case condition , the proposed method can potentially increase the expected rate of successful searches by 100 % . Moreover , the proposed search strategy outperforms a greedy search strategy , that considers only the users ' location probabilities and ignores their deadline constraints . Under certain conditions , the expected rate of successful searches generated by the proposed method is twice the equivalent rate generated by the greedy search strategy . Lastly , we perform repositioning of drugs to the top 20 % ranked diseases . ResultsThe results showed that F-measure of the proposed method was 0.75 , outperforming 0.5 of greedy searching for the entire diseases . For the utility of the proposed method , it was applied to dementia and verified 75 % accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia . To embrace network inconsistency , we propose ENDA , a three-tier architecture that leverages user track prediction , realtime network performance and server loads to optimize offloading decisions . On cloud tier , we first design a greedy searching algorithm to predict user track using historical user traces stored in database servers . We then design a cloud-enabled Wi-Fi access point ( AP ) selection scheme to find the most energy efficient AP for smartphone offloading . In this paper , the mathematical procedures and considerations for the optimization approach are presented for multi-label feature selection based on mutual information . We compared the proposed method with conventional greedy search based methods to show the potential of optimization based multi-label feature selection . In this paper , we propose the first two approaches to structure learning for PSL . We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways . The highly scalable optimization method combines data-driven generation of clauses with a piecewise pseudolikelihood ( PPLL ) objective that learns model structure by optimizing clause weights only once . Here , the training data set is obtained from iTOA itself as its own teacher . Compared with game theory and greedy search based methods , the proposed iTOA improves service latency performance by 33 % and 60 % , respectively . Automatic induction of decision tress and production rules from data to develop structure-activity relationship ( SAR ) models for toxicity prediction of chemicals has recently received much attention and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node . These approaches can be successful however the greedy search will necessarily miss regions of the search space . In the past years quite a lot of algorithms concerning frequent graph pattern mining have been published . In this paper an overview on the different methods for graph data mining is given , starting with the greedy searches proposed in the middle of the nineties . The ILP-based approaches are taken into account as well as ideas influenced by basket analyses proposed lately . Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention , and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node . These approaches can be successful ; however , the greedy search will necessarily miss regions of the search space . In this paper , a transparency paths constructing approach based on the gate-level netlist of cores is proposed . It searches the potential transparency paths using greedy searching strategy with FB-numbers as its heuristic information , and solves constraints and inconsistency by inserting basic cells , multiplexers and controlling gates . With these transparency paths , IP cores can transfer one test from their inputs to outputs per clock cycle consecutively and thus can be used in transparency-based test scheme to enable at-speed testing and decrease the overhead of dedicated wrappers and TAMs . Second , we show that CT search is an NP-hard problem and we present a greedy algorithm that combines spatial proximity and keyword proximity with an efficient filter . We use the promising value filter to select better candidate trajectories for inclusion in the CT and prune out non-promising trajectories in the Greedy search process . We also develop a lower bound and upper bound mechanism to efficiently compute the promising value of each trajectory , allowing our promising value based greedy algorithm to scale to large trajectory databases . Several interesting approaches have been proposed to handle the class imbalance issue in the literature and the Oblique Decision Tree ( ODT ) is one of them . Nevertheless , most standard ODT construction algorithms use a greedy search process ; while only very few works have addressed this induction problem using an evolutionary approach and this is done without really considering the class imbalance issue . To cope with this limitation , we propose in this paper a multi-objective evolutionary approach to find optimized ODTs for imbalanced binary classification . In fact , it proves not to be the global optimum more than 70 % of the time across all our simulations , implying that it 's very likely to be trapped at a local optimum . In this paper , we propose a Greedy-Power Algorithm to find out the best set of creatives , that is starting with the solution from the conventional Greedy Algorithm , one can perform another Greedy Algorithm search on top of it , with the option of a few or even infinite rounds . The Greedy-Power algorithm is guaranteed to be not worse , as it only moves in the direction to increase the goal function . The purpose of this research is to study the combination of simulated annealing and Greedy algorithms to create an itinerary . The initialization process of the simulated annealing algorithm uses Greedy algorithm searching technique and then proceeded with existing optimization step in simulated annealing method . This paper proposed a method of enhanced simulated annealing algorithm and the experiment results show the enhanced simulated annealing increase the effectiveness of simulated annealing algorithm which produce the shortest distance needed to travel places around an area . section : Introduction Natural language parsing can be formulated as a series of decisions that read words in sequence and incrementally combine them to form syntactic structures ; this formalization is known as transition - based parsing , and is often coupled with a greedy search procedure . The literature on transition - based parsing is vast , but all works share in common a classification component that takes into account features of the current parser state and predicts the next action to take conditioned on the state . Solutions based on curriculum learning , expected loss training , and reinforcement learning have been proposed . Finally , abandoning greedy search in favor of approximate global search offers an alternative solution to the problems with greedy search , and has been analyzed as well , including for parsing . section : Conclusions Note that the majority of batches is smaller because of the small size of the development set . We experiment with beams of size 5 as well as greedy search , i.e beam of size 1 . To make generation fast , we do not recompute convolution states that have not changed compared to the previous time step but rather copy ( shift ) these activations . However , the number of potential candidate systems is too large to allow an all - out combination , both for practical reasons and due to overfitting issues . Instead , we perform a greedy search , starting with the single best system , and successively adding additional systems , to find a small set of systems that are maximally complementary . The RT - 02 Switchboard set was used for this search procedure . We also markedly improve state - of - the - art on the BaBi dataset solving 20 / 20 tasks in 13 out of 15 runs with a single model trained jointly on all tasks . One potential issue with having a loss at every step is that it might encourage the network to learn a greedy algorithm that gets stuck in a local minima . However , the output function separates the node hidden states and messages from the output probability distributions . Denote the number of each banknote make the amount , such that the total number of banknotes used is minimized . Formally Since dilations ’s are multiples of each other , the simple greedy algorithm suffices to find out the shortest path spanning time steps . That is , first use the largest skip edge , , times , and then use the rest of the dilations to fit the residuals . To form a descriptive subset , we use sequential forward selection ( SFS ) . This is a greedy search algorithm that iteratively selects the feature that improves the recognition accuracy the most . A feature ’s usefulness is defined by the improvement it provides to recognition accuracy when used with the previously selected features . paragraph : JAMR Aligner . Two components exist in the JAMR aligner : 1 ) a set of heuristic rules and 2 ) a greedy search process . The heuristic rules in the JAMR aligner are a set of indicator functions which take a concept and a span of words starting from and ending with as input and return whether they should be aligned . The obtained results were further compared to those of deterministic and uncoordinated approaches . A comparison of the MCA algorithm to other optimization approaches such as particle swarm optimization ( PSO ) , genetic algorithm ( GA ) , and imperialist competitive algorithm ( ICA ) is also carried out . The simulation results and robustness analysis confirmed the superiority of the proposed probabilistic approach over various other approaches . This article proposes and verifies a novel intelligence approach for modelling forest fire danger , namely ICA-RVM , developed based on Relevance Vector Machine ( RVM ) and Imperialist Competitive Algorithm ( ICA ) , state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling . RVM is used to establish a prediction model that computes probability of fire danger , whereas ICA is adopted to optimize the prediction model . To verify the feasibility of the IPRS algorithm , localisation experiments were performed in 35 and 110 kV transformers , respectively . Compared with other present localisation methods , such as the Chan algorithm , the genetic algorithm and the imperial competitive algorithm , the proposed algorithm can effectively reduce localisation error . So its problem complexity is exponential that called NP-Complete problems . Imperial Competitive Algorithm is a new optimization method that commonly used for solving NP- Complete problems . In this paper Imperial Competitive Algorithm is used for solving Traveling Salesman Problems . Imperial Competitive Algorithm has been proved to be an efficient and effective meta-heuristic optimization algorithm and it has been successfully applied in many scientific and engineering problems . By introducing the concept of explorers and retention policy , the original algorithm is enhanced with a dynamic population mechanism in this paper and hence , the performance of the Imperial Competitive Algorithm is improved . Performance of the proposed modification is tested with experiments of optimizing real-values functions and results are compared with results obtained with the original Imperialistic Competitive Algorithm , Genetic Algorithm , Particle Swarm Optimization and Simulated Annealing . This paper presents an accurate Differential Global Positioning System ( DGPS ) using multi-layered Neural Networks ( NNs ) based on the Back Propagation ( BP ) and Imperialistic Competition Algorithm ( ICA ) in order to predict the DGPS corrections for accurate positioning . Simulation results allowed us to optimize the NN performance in term of residual mean square error . In this paper the Imperialistic competition algorithm ( ICA ) is used to minimize harmonic components of the output voltage up to 75th harmonic in a multilevel inverter structure with unequal DC sources by the means of selected switching angles . As the number of switching angles increase , it becomes more difficult to calculate the optimum switching angles in order to minimize THD , so the ICA method is used to find the global optimum of the search space . In this paper , seven different metaheuristic optimization algorithms developed between 2005 and 2012 are applied to optimize the robot trajectory for a three-revolute ( 3R ) robotic arm . These algorithms include the artificial bee colony ( ABC ) algorithm , biogeography-based optimization , gravitational search algorithm , cuckoo search algorithm ( CS ) , firefly algorithm , bat algorithm , and teaching-learning-based optimization ( TLBO ) . This work presents the optimization problem with the objective to plan a trajectory , which can minimize joint travelling time , joint travelling distance , and total joint Cartesian lengths , simultaneously . Biogeography-based optimization algorithm ( BBO ) is a relatively new optimization technique which has been shown to be competitive to other biology-based algorithms . However , there is still an insufficiency in BBO regarding its migration operator , which is good at exploitation but poor at exploration . In this paper , we present a novel solution to the VMcP problem called VMPMBBO . The proposed VMPMBBO treats VMcP problem as a complex system and utilizes the biogeography-based optimization ( BBO ) technique to optimize the virtual machine placement that minimizes both the resource wastage and the power consumption at the same time . Extensive experiments have been conducted using synthetic data from related literature and data from two real datasets . Motivated by the migration mechanisms of ecosystems , various extensions to biogeography-based optimization ( BBO ) are proposed . BBO is an original optimization method based on the mathematical model of organism distribution in biological systems . Traveling Salesman Problem ( TSP ) is an NP-hard combinatorial optimization problem with a wide variety of applications . Biogeography-Based Optimization Algorithm ( BBO ) is a new optimization technique , is inspired by geographical distribution of species within islands . The main idea of BBO based on migration strategy of species to derive algorithm for solving optimization problems . As the grid environments facilitate distributed computation , the scheduling of grid jobs has become an important issue . This study introduces a novel approach based on Biogeography Based Optimization algorithm ( BBO ) for scheduling jobs on computational grid . The proposed approach generates an optimal schedule so as to complete the jobs within a minimum period of time . The proposed approach provides high quality solution with respect to efficiency as well as shop floor stability . The idea of hybridizing the newly developed biogeography based optimization algorithm ( BBO ) with variable neighborhood structure ( VNS ) is proposed in order to produce a high performance initial schedule in terms of minimum completion time , tardiness and flow time within reasonable amount of time . Furthermore , due to the limitation of single rescheduling strategy to handle various disruptions , an approach that combines multiple rescheduling strategy is used to maintain efficiency and stability . For this purpose , web content in form of text and weblog features are analyzed . As per dynamic user behavior , proposed model LWPP-BOA ( Logistic Web Page Prediction By Biogeography Optimization Algorithm ) predict page by using genetic algorithm . Based on user actions , weblog feature are developed in form of association rules , while web content gives a set of relevant text patterns . This paper discusses the design of a multivariable control for unified power flow controller using evolutionary optimization algorithms . It utilizes two biologically inspired optimization algorithms ; the particle swarm optimization algorithm and biogeography optimization algorithms , to obtain the optimal set for the controllers of the UPFC . The UPFC is to control the active power flow through the line , regulate the AC bus voltage , regulate the DC link voltage , and damp the low frequency oscillations in the network through a set of PI controllers and a two stage lead lag compensator respectively . We call this problem the directed profitable rural postman problem with incompatibility constraints . We propose two problem formulations and introduce a matheuristic procedure exploiting the presence of a variant of the generalized independent set problem ( GISP ) and of the directed rural postman problem ( DRPP ) as subproblems . Computational results show how the matheuristic is effective outperforming in many cases the result obtained in one hour computing time by a straightforward branch-and-cut approach implemented with IBM CPLEX 12.6.2 on instances with up to 500 nodes , 1535 arcs , 1132 profitable arcs , and 10,743 incompatibilities . Extensive computational experiments are conducted on both randomly generated instances and instances that are adapted from the TSPLIB . The results show that our proposed matheuristic algorithm could be quite effective in finding good-quality solutions . In addition , we discuss an extended case wherein a whole set of different boards is to be assembled , which is highly relevant for low volume production with a high degree of customisation . After stating an extended integer linear programming ( ILP ) formulation , we propose two practical approaches for solving the computationally more complex second scenario : an order-based heuristic approach and a matheuristic applying a truncated variant of the ILP model with different sequencing strategies . The computational evaluation based on a real-world use case from the PCB industry underlines the efficacy of the matheuristic approach for obtaining a good overall makespan . However the literature is full of a variety of heuristic methodologies including the classical and the most modern hybrid approaches . Among the hybrid approaches , the most recent one noted is mat-heuristics that combine heuristics and mathematical programming techniques to solve combinatorial optimisation problems . The mat-heuristics approaches appear to be comparatively in its infant age at this point in time . However this is an exciting area of research which seeks more attention in the literature . At the level of the so-called key actions special External Advisory Groups are being foreseen . At the policy level some of the missions of ESTA ( European Science and Technology Assembly ) and IRDAC ( Industrial Research and Development Advisory Committee ) will survice in a new and scaled-down single advisory body However , due to Linked Data being relatively new field , currently there is a lack of tools that enable exploration and analysis of linked geospatial statistical datasets . This paper presents ESTA-LD ( Exploratory Spatio-Temporal Analysis ) , a tool that enables exploration and visualization of statistical data in linked data format , with an emphasis on spatial and temporal dimensions , thus allowing to visualize how different regions compare against each other , as well as how they evolved through time . Additionally , the paper discusses best practices for modeling spatial and temporal dimensions so that they conform to the established standards for representing space and time in The last station converts the received signals into digital signals THROUGH PROCESS circuitry included in your receiver . ESTA processing circuitry performs steps an iterative process and at each step DERIVED FROM A RECEIVED SIGNAL LIN THRESHOLD TO A RELATED TO A signal characteristic . THIS IS STORED VALUE THROUGH A DIGITAL CONVERTER AD analog to a memory MEM . Visual Prolog has already become a widely used AI programming language in logic and international developing tools and environment , which is a powerful , general-purpose , intelligent and integrated visual studio . ESTA V4.5 is a commercial Expert System Shell which is produced accompany with Visual Prolog V5.2.All aspects of ESTA and its Chinesed application research are presented in this paper . The transformer fault diagnosis Expert System(based on SANBIZHI rule)has been developed and applied by ETSA Chinesed . ESTA also can informs the user as to how the recommendation or conclusion was reached on the screen or on the print – out . ESTA modules procedures used by a forage expert in determining agro economic recommendations for successful establishment and production of alfalfa . With ESTA information is readily available to alfalfa producers for making management decisions relative to liming , fertilization , irrigation , drainage , weed control , longevity of stand , variety recommendations , method and rate of seeding . However , due to Linked Data being relatively a new field , currently there is a lack of tools that enable efficient exploration and analysis of linked geospatial statistical datasets . Therefore , ESTA-LD ( Exploratory Spatio-Temporal Analysis ) tool was developed to address some of the Linked statistical Data management issues , such as crossing the statistical and the geographical dimensions , producing statistical maps , visualizing different measures , and comparing statistical indicators of different regions through time . This paper discusses the modeling approach that was adopted so that the published data conform to the established standards for representing statistical , spatial and temporal data in Linked Data format . A detailed literature review revealed that , most of the organizations develop their projects and assoc iated software engineering activities using in-house esta blished methods . There are discrete references in t he literature on application of broad based scientific methods in designing and developing process capability and maturity models by software organizations . The improved algorithm , which is refereed as Earliest Start Time Algorithm with Partitioning ( ESTAP ) , divides the larger problem into smaller subproblems by partitioning the temporal space and then iteratively solves the subproblems . Benchmark problem comparisons with the classical ESTA formulation for two hundred tasks indicates that the proposed temporal space partitioning approach improves the computation time forty-fold while only incurring five percent increase in the total completion of the tasks . The Extended Super-Twisting Algorithm ( ESTA ) is a second order sliding mode controller generating continuous control signal . ESTA could be designed with arbitrary high homogeneity weight of output compensating theoretically exactly Lipschitz perturbations in the systems with relative degree one . Here , I summarize a potentially major deficiency in how the relationship between exact and heuristic methods has been presented : the above comparisons have all been done using relatively simple ( linear ) maximum coverage or minimum set models that are by definition solvable using integer programming . In contrast , heuristic or meta-heuristic algorithms can be applied to less simplified nonlinear and/or stochastic problems . The focus of this study is two kinds of suboptimality , first-stage suboptimality caused by model simplification and second-stage suboptimality caused by inexact solution . This challenge is referred to as the coordinated NFV resource allocation ( coordinated NFV-RA ) problem which is proved to be NP-hard . In order to response timely to the service variation , many heuristic or meta-heuristic algorithms are proposed to reduce the computing complexity . However , it is very difficult to evaluate the approach degree between obtained sub-optimal solutions and the optima , since finding the optimal solution is a non-trivial task . To reduce the transportation and inventory costs , therefore , this paper establishes a mixed integer programming model for the joint optimization of multibatch production and vehicle routing problems involving a pickup . Following this , a two-stage hybrid heuristic algorithm is proposed to solve this model . In the first stage , an integrated algorithm , combining the Clarke-Wright ( CW ) algorithm and the Record to Record ( RTR ) travel algorithm , was used to solve vehicle routing problem . In the second stage , the Particle Swarm Optimization ( PSO ) algorithm was used to allocate vehicles to each production batch . Multiple sets of numerical experiments were then performed to validate the effectiveness of the proposed model and the performance efficiency of the two-stage hybrid heuristic algorithm . To construct the feasible train schedule , the proposed SE algorithm consists of many individual modules including the feasibility-satisfaction procedure , time-determination procedure , tune-up procedure and conflict-resolve procedure algorithms . To find a good train schedule , a two-stage hybrid heuristic algorithm called the SE-BIH algorithm is developed by combining the constructive heuristic ( i.e. the SE algorithm ) and the local-search heuristic ( i.e. the Best-Insertion- Heuristic algorithm ) . To optimise the train schedule , a three-stage algorithm called the SE-BIH-TS algorithm is developed by combining the tabu search ( TS ) metaheuristic with the SE-BIH algorithm . Offline selection hyper-heuristics are machine learning methods that are trained on heuristic selections to create an algorithm that is tuned for a particular problem domain . In this work , a simple selection hyper-heuristic is executed on a number of computationally hard benchmark optimisation problems , and the resulting sequences of low level heuristic selections and objective function values are used to construct an offline learning database . An Elman network is trained on sequences of heuristic selections chosen from the offline database and the network ’s ability to learn and generalise from these sequences is evaluated . A selection hyper-heuristic is used to minimise the objective functions of a well-known set of benchmark problems . The resulting sequences of low level heuristic selections and objective function values are used to generate a database of heuristic selections . The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between “ effective ” subsequences , which tend to decrease the objective value , and “ disruptive ” subsequences , which tend to increase the objective value . PRIM-MS falls into bilevel programming in the form of leader-follower decisions , where the attacker tries to degrade system performance the most by determining offensive resources invested while the user rebuilds supply-demand relationship to reduce the influences by interdiction . In this paper , we model the problem as a single level heuristic model based on two heuristic strategies . Experiments are carried out to testify the effectiveness of the heuristic model . We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria . We then provide a novel diversity based heuristic to perform task-driven selection of landmark points instead of random selection . We demonstrate the effectiveness of our goodness criteria learning method as well as the landmark selection heuristic on a variety of similarity-based learning datasets and benchmark UCI datasets on which our method consistently outperforms existing approaches by a significant margin . Existing Commercial clouds provide strong consistency guarantees but it is hard for the users to verify it . This paper proposes a Novel Heuristic Auditor based on loosely synchronized clocks which help the users to verify whether the data cloud provides the assured level of consistency as stated in the SLA . It uses a two level auditing structure to check for strong consistency violations namely Read-After-Write(RAW ) consistency and Monotonic-Write(MW ) consistency . Erdfelder , KA¼pper-Tetzel , and Mattern ( 2011 ) questioned whether the recognition judgment itself affects decisions or rather the memory strength underlying it . Specifically , they proposed to extend the RH to the memory state heuristic ( MSH ) , which assumes a third memory state of uncertainty in addition to recognition certainty and rejection certainty . While the MSH already gathered significant support , one of its basic and more counterintuitive predictions has not been tested so far : In guessing pairs ( none of the objects recognized ) , the object more slowly judged as unrecognized should be preferred , since it is more likely to be in a higher memory state . The problem that we saw was the gap between the view of beginners through to competent learners and the view of experts-to-be : There did not seem to be a hint of how learners pass from one level to the other , nor a unified picture of the strategies required for educating experts or practitioners . Out of this need , we developed the heuristic cube model [ Fig. 1 ] that combines the (meta)contents of learning with the goals of the learner and the learning strategies ( see [ Baumgartner 1991 ] , [ Baumgartner 1992 ] , [ Baumgartner 1993 ] , [ Baumgartner 1995 ] , [ Baumgartner and Payr 1994 ] , [ Baumgartner and Payr 1995b ] ) . develop act explore understand decide select apply imitate perceive remember teach , explain ( teacher ) observe , help In the present paper we discuss a multi-heuristic optimization technique for hard combinatorial optimization problems and outline its algorithmic skeleton . The proposed technique is based on a combination of heuristics commonly used in the area of combinatorial optimization and two branch-and-bound methods . The proposed method is based on an algorithm which mimics the evolution of a type of mold called physarum . Our deployment problem is close to a Minimum Steiner Tree ( MST ) problem known to be NP-hard , we thus compare our results to a heuristic of MST . Moves are therefore ordered by iterative deepening ( using a shallower search to order moves for a deeper search ) . In addition , a combination of domain - independent move ordering heuristics , such as killer heuristic , history heuristic , counter - move heuristic , and also domain - dependent knowledge based on captures ( SEE ) and potential captures ( MVV / LVA ) . A transposition table facilitates the reuse of values and move orders when the same position is reached by multiple paths . The most closely related to ours is the associative embedding work of , which demonstrated strong results for grouping multi - person keypoints , and unpublished work from on metric learning for instance segmentation . Our approach extends on these ideas substantially by integrating recurrent mean - shift to directly generate the final instances ( rather than heuristic decoding or thresholding distance to seed proposals ) . There is also an important and interesting connection to work that has used embedding to separate instances where the embedding is directly learned using a supervised regression loss rather than a pairwise associative loss . On the left we see that after 3 passes the heuristic converges to a local optimum . In the middle we see that when repeating the heuristic algorithm a couple of times with different random initializations , and keeping the best path after each iteration , the loglikelihood converges to the optimum . In our experiments we initialized the heuristic with the optimal path from the previous E - step ( warm start ) and performed the heuristic algorithm for 1 pass . The same calculation is performed for and . Thus , we collect knowledge - enriched local inference information : where a heuristic matching trick with difference and element - wise product is used DBLP : conf / acl / MouMLX0YJ16 , DBLP : conf / acl / ChenZLWJI17 . The last terms in Equation ( [ reference ] ) ( [ reference ] ) are used to obtain word - level inference information from external knowledge . For DuReader , the retrieved document usually contains a large number of paragraphs that can not be fed into MRC models directly . The original paper employs a simple a simple heuristic strategy to select a representative paragraph for each document , while we train a paragraph ranking model for this . We will demonstrate the effects of these two technologies later . Our SteinGAN instead performs traditional MLE training for a deep energy model , with the help of a neural sampler that learns to draw samples from the energy model to approximate the likelihood function ; this admits an adversarial interpretation : we can view the neural sampler as a generator that attends to fool the deep energy model , which in turn serves as a discriminator that distinguishes the real samples and the simulated samples given by the neural sampler . This idea of training MLE with neural samplers was first discussed by [ reference ] ; one of the key differences is that the neural sampler in [ reference ] is trained with the help of a heuristic diversity regularizer based on batch normalization , while SVGD enforces the diversity in a more principled way . Another method by [ reference ] also trains an energy score to distinguish real and simulated samples , but within a non - probabilistic framework ( see Section 5 for more discussion ) . It has been shown to be NP-complete , and is an often-used benchmark for new optimization techniques . One of the main challenges with this problem is that standard , non-AI heuristic approaches such as the Lin-Kernighan algorithm ( LK ) and the chained LK variant are currently very effective and in wide use for the common fully connected , Euclidean variant that is considered here . This paper presents an algorithm that uses adaptive resonance theory ( ART ) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP .
Finally LSBN employs an efficient algorithm , to merge structures of overlapping communities into a whole . In comparison with other four state-of-art large-scale network structure learning algorithms such as ARACNE , PC , Greedy Search and MMHC , LSBN shows comparable results in five common benchmark datasets , evaluated by precision , recall and f-score . What 's more , LSBN makes it possible to learn large-scale Bayesian structure by Model Averaging which used to be intractable .
However , the relaxation can only be applied to two-class cases . In this paper , we propose full eigenvector analysis of p-Laplacian and obtain a natural global embedding for multi-class clustering problems , instead of using greedy search strategy implemented by previous researchers . An efficient gradient descend optimization approach is introduced to obtain the p-Laplacian embedding space , which is guaranteed to converge to feasible local solutions .
Even under the worst case condition , the proposed method can potentially increase the expected rate of successful searches by 100 % . Moreover , the proposed search strategy outperforms a greedy search strategy , that considers only the users ' location probabilities and ignores their deadline constraints . Under certain conditions , the expected rate of successful searches generated by the proposed method is twice the equivalent rate generated by the greedy search strategy .
Lastly , we perform repositioning of drugs to the top 20 % ranked diseases . ResultsThe results showed that F-measure of the proposed method was 0.75 , outperforming 0.5 of greedy searching for the entire diseases . For the utility of the proposed method , it was applied to dementia and verified 75 % accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia .
To embrace network inconsistency , we propose ENDA , a three-tier architecture that leverages user track prediction , realtime network performance and server loads to optimize offloading decisions . On cloud tier , we first design a greedy searching algorithm to predict user track using historical user traces stored in database servers . We then design a cloud-enabled Wi-Fi access point ( AP ) selection scheme to find the most energy efficient AP for smartphone offloading .
In this paper , the mathematical procedures and considerations for the optimization approach are presented for multi-label feature selection based on mutual information . We compared the proposed method with conventional greedy search based methods to show the potential of optimization based multi-label feature selection .
In this paper , we propose the first two approaches to structure learning for PSL . We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways . The highly scalable optimization method combines data-driven generation of clauses with a piecewise pseudolikelihood ( PPLL ) objective that learns model structure by optimizing clause weights only once .
Here , the training data set is obtained from iTOA itself as its own teacher . Compared with game theory and greedy search based methods , the proposed iTOA improves service latency performance by 33 % and 60 % , respectively .
Automatic induction of decision tress and production rules from data to develop structure-activity relationship ( SAR ) models for toxicity prediction of chemicals has recently received much attention and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node . These approaches can be successful however the greedy search will necessarily miss regions of the search space .
In the past years quite a lot of algorithms concerning frequent graph pattern mining have been published . In this paper an overview on the different methods for graph data mining is given , starting with the greedy searches proposed in the middle of the nineties . The ILP-based approaches are taken into account as well as ideas influenced by basket analyses proposed lately .
Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention , and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node . These approaches can be successful ; however , the greedy search will necessarily miss regions of the search space .
In this paper , a transparency paths constructing approach based on the gate-level netlist of cores is proposed . It searches the potential transparency paths using greedy searching strategy with FB-numbers as its heuristic information , and solves constraints and inconsistency by inserting basic cells , multiplexers and controlling gates . With these transparency paths , IP cores can transfer one test from their inputs to outputs per clock cycle consecutively and thus can be used in transparency-based test scheme to enable at-speed testing and decrease the overhead of dedicated wrappers and TAMs .
Second , we show that CT search is an NP-hard problem and we present a greedy algorithm that combines spatial proximity and keyword proximity with an efficient filter . We use the promising value filter to select better candidate trajectories for inclusion in the CT and prune out non-promising trajectories in the Greedy search process . We also develop a lower bound and upper bound mechanism to efficiently compute the promising value of each trajectory , allowing our promising value based greedy algorithm to scale to large trajectory databases .
Several interesting approaches have been proposed to handle the class imbalance issue in the literature and the Oblique Decision Tree ( ODT ) is one of them . Nevertheless , most standard ODT construction algorithms use a greedy search process ; while only very few works have addressed this induction problem using an evolutionary approach and this is done without really considering the class imbalance issue . To cope with this limitation , we propose in this paper a multi-objective evolutionary approach to find optimized ODTs for imbalanced binary classification .
In fact , it proves not to be the global optimum more than 70 % of the time across all our simulations , implying that it 's very likely to be trapped at a local optimum . In this paper , we propose a Greedy-Power Algorithm to find out the best set of creatives , that is starting with the solution from the conventional Greedy Algorithm , one can perform another Greedy Algorithm search on top of it , with the option of a few or even infinite rounds . The Greedy-Power algorithm is guaranteed to be not worse , as it only moves in the direction to increase the goal function .
The purpose of this research is to study the combination of simulated annealing and Greedy algorithms to create an itinerary . The initialization process of the simulated annealing algorithm uses Greedy algorithm searching technique and then proceeded with existing optimization step in simulated annealing method . This paper proposed a method of enhanced simulated annealing algorithm and the experiment results show the enhanced simulated annealing increase the effectiveness of simulated annealing algorithm which produce the shortest distance needed to travel places around an area .
section : Introduction Natural language parsing can be formulated as a series of decisions that read words in sequence and incrementally combine them to form syntactic structures ; this formalization is known as transition - based parsing , and is often coupled with a greedy search procedure . The literature on transition - based parsing is vast , but all works share in common a classification component that takes into account features of the current parser state and predicts the next action to take conditioned on the state .
Solutions based on curriculum learning , expected loss training , and reinforcement learning have been proposed . Finally , abandoning greedy search in favor of approximate global search offers an alternative solution to the problems with greedy search , and has been analyzed as well , including for parsing . section : Conclusions
Note that the majority of batches is smaller because of the small size of the development set . We experiment with beams of size 5 as well as greedy search , i.e beam of size 1 . To make generation fast , we do not recompute convolution states that have not changed compared to the previous time step but rather copy ( shift ) these activations .
However , the number of potential candidate systems is too large to allow an all - out combination , both for practical reasons and due to overfitting issues . Instead , we perform a greedy search , starting with the single best system , and successively adding additional systems , to find a small set of systems that are maximally complementary . The RT - 02 Switchboard set was used for this search procedure .
We also markedly improve state - of - the - art on the BaBi dataset solving 20 / 20 tasks in 13 out of 15 runs with a single model trained jointly on all tasks . One potential issue with having a loss at every step is that it might encourage the network to learn a greedy algorithm that gets stuck in a local minima . However , the output function separates the node hidden states and messages from the output probability distributions .
Denote the number of each banknote make the amount , such that the total number of banknotes used is minimized . Formally Since dilations ’s are multiples of each other , the simple greedy algorithm suffices to find out the shortest path spanning time steps . That is , first use the largest skip edge , , times , and then use the rest of the dilations to fit the residuals .
To form a descriptive subset , we use sequential forward selection ( SFS ) . This is a greedy search algorithm that iteratively selects the feature that improves the recognition accuracy the most . A feature ’s usefulness is defined by the improvement it provides to recognition accuracy when used with the previously selected features .
paragraph : JAMR Aligner . Two components exist in the JAMR aligner : 1 ) a set of heuristic rules and 2 ) a greedy search process . The heuristic rules in the JAMR aligner are a set of indicator functions which take a concept and a span of words starting from and ending with as input and return whether they should be aligned .
The obtained results were further compared to those of deterministic and uncoordinated approaches . A comparison of the MCA algorithm to other optimization approaches such as particle swarm optimization ( PSO ) , genetic algorithm ( GA ) , and imperialist competitive algorithm ( ICA ) is also carried out . The simulation results and robustness analysis confirmed the superiority of the proposed probabilistic approach over various other approaches .
This article proposes and verifies a novel intelligence approach for modelling forest fire danger , namely ICA-RVM , developed based on Relevance Vector Machine ( RVM ) and Imperialist Competitive Algorithm ( ICA ) , state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling . RVM is used to establish a prediction model that computes probability of fire danger , whereas ICA is adopted to optimize the prediction model .
To verify the feasibility of the IPRS algorithm , localisation experiments were performed in 35 and 110 kV transformers , respectively . Compared with other present localisation methods , such as the Chan algorithm , the genetic algorithm and the imperial competitive algorithm , the proposed algorithm can effectively reduce localisation error .
So its problem complexity is exponential that called NP-Complete problems . Imperial Competitive Algorithm is a new optimization method that commonly used for solving NP- Complete problems . In this paper Imperial Competitive Algorithm is used for solving Traveling Salesman Problems .
Imperial Competitive Algorithm has been proved to be an efficient and effective meta-heuristic optimization algorithm and it has been successfully applied in many scientific and engineering problems . By introducing the concept of explorers and retention policy , the original algorithm is enhanced with a dynamic population mechanism in this paper and hence , the performance of the Imperial Competitive Algorithm is improved . Performance of the proposed modification is tested with experiments of optimizing real-values functions and results are compared with results obtained with the original Imperialistic Competitive Algorithm , Genetic Algorithm , Particle Swarm Optimization and Simulated Annealing .
This paper presents an accurate Differential Global Positioning System ( DGPS ) using multi-layered Neural Networks ( NNs ) based on the Back Propagation ( BP ) and Imperialistic Competition Algorithm ( ICA ) in order to predict the DGPS corrections for accurate positioning . Simulation results allowed us to optimize the NN performance in term of residual mean square error .
In this paper the Imperialistic competition algorithm ( ICA ) is used to minimize harmonic components of the output voltage up to 75th harmonic in a multilevel inverter structure with unequal DC sources by the means of selected switching angles . As the number of switching angles increase , it becomes more difficult to calculate the optimum switching angles in order to minimize THD , so the ICA method is used to find the global optimum of the search space .
In this paper , seven different metaheuristic optimization algorithms developed between 2005 and 2012 are applied to optimize the robot trajectory for a three-revolute ( 3R ) robotic arm . These algorithms include the artificial bee colony ( ABC ) algorithm , biogeography-based optimization , gravitational search algorithm , cuckoo search algorithm ( CS ) , firefly algorithm , bat algorithm , and teaching-learning-based optimization ( TLBO ) . This work presents the optimization problem with the objective to plan a trajectory , which can minimize joint travelling time , joint travelling distance , and total joint Cartesian lengths , simultaneously .
Biogeography-based optimization algorithm ( BBO ) is a relatively new optimization technique which has been shown to be competitive to other biology-based algorithms . However , there is still an insufficiency in BBO regarding its migration operator , which is good at exploitation but poor at exploration .
In this paper , we present a novel solution to the VMcP problem called VMPMBBO . The proposed VMPMBBO treats VMcP problem as a complex system and utilizes the biogeography-based optimization ( BBO ) technique to optimize the virtual machine placement that minimizes both the resource wastage and the power consumption at the same time . Extensive experiments have been conducted using synthetic data from related literature and data from two real datasets .
Motivated by the migration mechanisms of ecosystems , various extensions to biogeography-based optimization ( BBO ) are proposed . BBO is an original optimization method based on the mathematical model of organism distribution in biological systems .
Traveling Salesman Problem ( TSP ) is an NP-hard combinatorial optimization problem with a wide variety of applications . Biogeography-Based Optimization Algorithm ( BBO ) is a new optimization technique , is inspired by geographical distribution of species within islands . The main idea of BBO based on migration strategy of species to derive algorithm for solving optimization problems .
As the grid environments facilitate distributed computation , the scheduling of grid jobs has become an important issue . This study introduces a novel approach based on Biogeography Based Optimization algorithm ( BBO ) for scheduling jobs on computational grid . The proposed approach generates an optimal schedule so as to complete the jobs within a minimum period of time .
The proposed approach provides high quality solution with respect to efficiency as well as shop floor stability . The idea of hybridizing the newly developed biogeography based optimization algorithm ( BBO ) with variable neighborhood structure ( VNS ) is proposed in order to produce a high performance initial schedule in terms of minimum completion time , tardiness and flow time within reasonable amount of time . Furthermore , due to the limitation of single rescheduling strategy to handle various disruptions , an approach that combines multiple rescheduling strategy is used to maintain efficiency and stability .
For this purpose , web content in form of text and weblog features are analyzed . As per dynamic user behavior , proposed model LWPP-BOA ( Logistic Web Page Prediction By Biogeography Optimization Algorithm ) predict page by using genetic algorithm . Based on user actions , weblog feature are developed in form of association rules , while web content gives a set of relevant text patterns .
This paper discusses the design of a multivariable control for unified power flow controller using evolutionary optimization algorithms . It utilizes two biologically inspired optimization algorithms ; the particle swarm optimization algorithm and biogeography optimization algorithms , to obtain the optimal set for the controllers of the UPFC . The UPFC is to control the active power flow through the line , regulate the AC bus voltage , regulate the DC link voltage , and damp the low frequency oscillations in the network through a set of PI controllers and a two stage lead lag compensator respectively .
We call this problem the directed profitable rural postman problem with incompatibility constraints . We propose two problem formulations and introduce a matheuristic procedure exploiting the presence of a variant of the generalized independent set problem ( GISP ) and of the directed rural postman problem ( DRPP ) as subproblems . Computational results show how the matheuristic is effective outperforming in many cases the result obtained in one hour computing time by a straightforward branch-and-cut approach implemented with IBM CPLEX 12.6.2 on instances with up to 500 nodes , 1535 arcs , 1132 profitable arcs , and 10,743 incompatibilities .
Extensive computational experiments are conducted on both randomly generated instances and instances that are adapted from the TSPLIB . The results show that our proposed matheuristic algorithm could be quite effective in finding good-quality solutions .
In addition , we discuss an extended case wherein a whole set of different boards is to be assembled , which is highly relevant for low volume production with a high degree of customisation . After stating an extended integer linear programming ( ILP ) formulation , we propose two practical approaches for solving the computationally more complex second scenario : an order-based heuristic approach and a matheuristic applying a truncated variant of the ILP model with different sequencing strategies . The computational evaluation based on a real-world use case from the PCB industry underlines the efficacy of the matheuristic approach for obtaining a good overall makespan .
However the literature is full of a variety of heuristic methodologies including the classical and the most modern hybrid approaches . Among the hybrid approaches , the most recent one noted is mat-heuristics that combine heuristics and mathematical programming techniques to solve combinatorial optimisation problems . The mat-heuristics approaches appear to be comparatively in its infant age at this point in time . However this is an exciting area of research which seeks more attention in the literature .
At the level of the so-called key actions special External Advisory Groups are being foreseen . At the policy level some of the missions of ESTA ( European Science and Technology Assembly ) and IRDAC ( Industrial Research and Development Advisory Committee ) will survice in a new and scaled-down single advisory body
However , due to Linked Data being relatively new field , currently there is a lack of tools that enable exploration and analysis of linked geospatial statistical datasets . This paper presents ESTA-LD ( Exploratory Spatio-Temporal Analysis ) , a tool that enables exploration and visualization of statistical data in linked data format , with an emphasis on spatial and temporal dimensions , thus allowing to visualize how different regions compare against each other , as well as how they evolved through time . Additionally , the paper discusses best practices for modeling spatial and temporal dimensions so that they conform to the established standards for representing space and time in
The last station converts the received signals into digital signals THROUGH PROCESS circuitry included in your receiver . ESTA processing circuitry performs steps an iterative process and at each step DERIVED FROM A RECEIVED SIGNAL LIN THRESHOLD TO A RELATED TO A signal characteristic . THIS IS STORED VALUE THROUGH A DIGITAL CONVERTER AD analog to a memory MEM .
Visual Prolog has already become a widely used AI programming language in logic and international developing tools and environment , which is a powerful , general-purpose , intelligent and integrated visual studio . ESTA V4.5 is a commercial Expert System Shell which is produced accompany with Visual Prolog V5.2.All aspects of ESTA and its Chinesed application research are presented in this paper . The transformer fault diagnosis Expert System(based on SANBIZHI rule)has been developed and applied by ETSA Chinesed .
ESTA also can informs the user as to how the recommendation or conclusion was reached on the screen or on the print – out . ESTA modules procedures used by a forage expert in determining agro economic recommendations for successful establishment and production of alfalfa . With ESTA information is readily available to alfalfa producers for making management decisions relative to liming , fertilization , irrigation , drainage , weed control , longevity of stand , variety recommendations , method and rate of seeding .
However , due to Linked Data being relatively a new field , currently there is a lack of tools that enable efficient exploration and analysis of linked geospatial statistical datasets . Therefore , ESTA-LD ( Exploratory Spatio-Temporal Analysis ) tool was developed to address some of the Linked statistical Data management issues , such as crossing the statistical and the geographical dimensions , producing statistical maps , visualizing different measures , and comparing statistical indicators of different regions through time . This paper discusses the modeling approach that was adopted so that the published data conform to the established standards for representing statistical , spatial and temporal data in Linked Data format .
A detailed literature review revealed that , most of the organizations develop their projects and assoc iated software engineering activities using in-house esta blished methods . There are discrete references in t he literature on application of broad based scientific methods in designing and developing process capability and maturity models by software organizations .
The improved algorithm , which is refereed as Earliest Start Time Algorithm with Partitioning ( ESTAP ) , divides the larger problem into smaller subproblems by partitioning the temporal space and then iteratively solves the subproblems . Benchmark problem comparisons with the classical ESTA formulation for two hundred tasks indicates that the proposed temporal space partitioning approach improves the computation time forty-fold while only incurring five percent increase in the total completion of the tasks .
The Extended Super-Twisting Algorithm ( ESTA ) is a second order sliding mode controller generating continuous control signal . ESTA could be designed with arbitrary high homogeneity weight of output compensating theoretically exactly Lipschitz perturbations in the systems with relative degree one .
Here , I summarize a potentially major deficiency in how the relationship between exact and heuristic methods has been presented : the above comparisons have all been done using relatively simple ( linear ) maximum coverage or minimum set models that are by definition solvable using integer programming . In contrast , heuristic or meta-heuristic algorithms can be applied to less simplified nonlinear and/or stochastic problems . The focus of this study is two kinds of suboptimality , first-stage suboptimality caused by model simplification and second-stage suboptimality caused by inexact solution .
This challenge is referred to as the coordinated NFV resource allocation ( coordinated NFV-RA ) problem which is proved to be NP-hard . In order to response timely to the service variation , many heuristic or meta-heuristic algorithms are proposed to reduce the computing complexity . However , it is very difficult to evaluate the approach degree between obtained sub-optimal solutions and the optima , since finding the optimal solution is a non-trivial task .
To reduce the transportation and inventory costs , therefore , this paper establishes a mixed integer programming model for the joint optimization of multibatch production and vehicle routing problems involving a pickup . Following this , a two-stage hybrid heuristic algorithm is proposed to solve this model . In the first stage , an integrated algorithm , combining the Clarke-Wright ( CW ) algorithm and the Record to Record ( RTR ) travel algorithm , was used to solve vehicle routing problem . In the second stage , the Particle Swarm Optimization ( PSO ) algorithm was used to allocate vehicles to each production batch . Multiple sets of numerical experiments were then performed to validate the effectiveness of the proposed model and the performance efficiency of the two-stage hybrid heuristic algorithm .
To construct the feasible train schedule , the proposed SE algorithm consists of many individual modules including the feasibility-satisfaction procedure , time-determination procedure , tune-up procedure and conflict-resolve procedure algorithms . To find a good train schedule , a two-stage hybrid heuristic algorithm called the SE-BIH algorithm is developed by combining the constructive heuristic ( i.e. the SE algorithm ) and the local-search heuristic ( i.e. the Best-Insertion- Heuristic algorithm ) . To optimise the train schedule , a three-stage algorithm called the SE-BIH-TS algorithm is developed by combining the tabu search ( TS ) metaheuristic with the SE-BIH algorithm .
Offline selection hyper-heuristics are machine learning methods that are trained on heuristic selections to create an algorithm that is tuned for a particular problem domain . In this work , a simple selection hyper-heuristic is executed on a number of computationally hard benchmark optimisation problems , and the resulting sequences of low level heuristic selections and objective function values are used to construct an offline learning database . An Elman network is trained on sequences of heuristic selections chosen from the offline database and the network ’s ability to learn and generalise from these sequences is evaluated .
A selection hyper-heuristic is used to minimise the objective functions of a well-known set of benchmark problems . The resulting sequences of low level heuristic selections and objective function values are used to generate a database of heuristic selections . The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between “ effective ” subsequences , which tend to decrease the objective value , and “ disruptive ” subsequences , which tend to increase the objective value .
PRIM-MS falls into bilevel programming in the form of leader-follower decisions , where the attacker tries to degrade system performance the most by determining offensive resources invested while the user rebuilds supply-demand relationship to reduce the influences by interdiction . In this paper , we model the problem as a single level heuristic model based on two heuristic strategies . Experiments are carried out to testify the effectiveness of the heuristic model .
We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria . We then provide a novel diversity based heuristic to perform task-driven selection of landmark points instead of random selection . We demonstrate the effectiveness of our goodness criteria learning method as well as the landmark selection heuristic on a variety of similarity-based learning datasets and benchmark UCI datasets on which our method consistently outperforms existing approaches by a significant margin .
Existing Commercial clouds provide strong consistency guarantees but it is hard for the users to verify it . This paper proposes a Novel Heuristic Auditor based on loosely synchronized clocks which help the users to verify whether the data cloud provides the assured level of consistency as stated in the SLA . It uses a two level auditing structure to check for strong consistency violations namely Read-After-Write(RAW ) consistency and Monotonic-Write(MW ) consistency .
Erdfelder , KA¼pper-Tetzel , and Mattern ( 2011 ) questioned whether the recognition judgment itself affects decisions or rather the memory strength underlying it . Specifically , they proposed to extend the RH to the memory state heuristic ( MSH ) , which assumes a third memory state of uncertainty in addition to recognition certainty and rejection certainty . While the MSH already gathered significant support , one of its basic and more counterintuitive predictions has not been tested so far : In guessing pairs ( none of the objects recognized ) , the object more slowly judged as unrecognized should be preferred , since it is more likely to be in a higher memory state .
The problem that we saw was the gap between the view of beginners through to competent learners and the view of experts-to-be : There did not seem to be a hint of how learners pass from one level to the other , nor a unified picture of the strategies required for educating experts or practitioners . Out of this need , we developed the heuristic cube model [ Fig. 1 ] that combines the (meta)contents of learning with the goals of the learner and the learning strategies ( see [ Baumgartner 1991 ] , [ Baumgartner 1992 ] , [ Baumgartner 1993 ] , [ Baumgartner 1995 ] , [ Baumgartner and Payr 1994 ] , [ Baumgartner and Payr 1995b ] ) . develop act explore understand decide select apply imitate perceive remember teach , explain ( teacher ) observe , help
In the present paper we discuss a multi-heuristic optimization technique for hard combinatorial optimization problems and outline its algorithmic skeleton . The proposed technique is based on a combination of heuristics commonly used in the area of combinatorial optimization and two branch-and-bound methods .
The proposed method is based on an algorithm which mimics the evolution of a type of mold called physarum . Our deployment problem is close to a Minimum Steiner Tree ( MST ) problem known to be NP-hard , we thus compare our results to a heuristic of MST .
Moves are therefore ordered by iterative deepening ( using a shallower search to order moves for a deeper search ) . In addition , a combination of domain - independent move ordering heuristics , such as killer heuristic , history heuristic , counter - move heuristic , and also domain - dependent knowledge based on captures ( SEE ) and potential captures ( MVV / LVA ) . A transposition table facilitates the reuse of values and move orders when the same position is reached by multiple paths .
The most closely related to ours is the associative embedding work of , which demonstrated strong results for grouping multi - person keypoints , and unpublished work from on metric learning for instance segmentation . Our approach extends on these ideas substantially by integrating recurrent mean - shift to directly generate the final instances ( rather than heuristic decoding or thresholding distance to seed proposals ) . There is also an important and interesting connection to work that has used embedding to separate instances where the embedding is directly learned using a supervised regression loss rather than a pairwise associative loss .
On the left we see that after 3 passes the heuristic converges to a local optimum . In the middle we see that when repeating the heuristic algorithm a couple of times with different random initializations , and keeping the best path after each iteration , the loglikelihood converges to the optimum . In our experiments we initialized the heuristic with the optimal path from the previous E - step ( warm start ) and performed the heuristic algorithm for 1 pass .
The same calculation is performed for and . Thus , we collect knowledge - enriched local inference information : where a heuristic matching trick with difference and element - wise product is used DBLP : conf / acl / MouMLX0YJ16 , DBLP : conf / acl / ChenZLWJI17 . The last terms in Equation ( [ reference ] ) ( [ reference ] ) are used to obtain word - level inference information from external knowledge .
For DuReader , the retrieved document usually contains a large number of paragraphs that can not be fed into MRC models directly . The original paper employs a simple a simple heuristic strategy to select a representative paragraph for each document , while we train a paragraph ranking model for this . We will demonstrate the effects of these two technologies later .
Our SteinGAN instead performs traditional MLE training for a deep energy model , with the help of a neural sampler that learns to draw samples from the energy model to approximate the likelihood function ; this admits an adversarial interpretation : we can view the neural sampler as a generator that attends to fool the deep energy model , which in turn serves as a discriminator that distinguishes the real samples and the simulated samples given by the neural sampler . This idea of training MLE with neural samplers was first discussed by [ reference ] ; one of the key differences is that the neural sampler in [ reference ] is trained with the help of a heuristic diversity regularizer based on batch normalization , while SVGD enforces the diversity in a more principled way . Another method by [ reference ] also trains an energy score to distinguish real and simulated samples , but within a non - probabilistic framework ( see Section 5 for more discussion ) .
It has been shown to be NP-complete , and is an often-used benchmark for new optimization techniques . One of the main challenges with this problem is that standard , non-AI heuristic approaches such as the Lin-Kernighan algorithm ( LK ) and the chained LK variant are currently very effective and in wide use for the common fully connected , Euclidean variant that is considered here . This paper presents an algorithm that uses adaptive resonance theory ( ART ) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP .
LSBN: A Large-Scale Bayesian Structure Learning Framework for Model Averaging On the eigenvectors of p-Laplacian On-line search for mobile users Network mirroring for drug repositioning ENDA: embracing network inconsistency for dynamic application offloading in mobile cloud computing Convex optimization approach for multi-label feature selection based on mutual information Scalable Structure Learning for Probabilistic Soft Logic An Intelligent Task Offloading Algorithm (iTOA) for UAV Network Induction of decision trees using genetic programming for the development of SAR toxicity models Graph based molecular data mining - an overview Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data Constructing transparency paths for IP cores using greedy searching strategy Efficient Trajectory Cover Search for Moving Object Trajectories Multi-objective evolution of oblique decision trees for imbalanced data binary classification How to select the best set of ads: Can we do better than Greedy Algorithm? Itinerary Recommendation Generation using Enhanced Simulated Annealing Algorithm Training with Exploration Improves a Greedy Stack LSTM Parser Convolutional Sequence to Sequence Learning Achieving Human Parity in Conversational Speech Recognition Recurrent Relational Networks Dilated Recurrent Neural Networks Greedy search for descriptive spatial face features An AMR Aligner Tuned by Transition-based Parser A Probabilistic Robust Coordinated Approach to Stabilize Power Oscillations in DFIG-Based Power Systems A Hybrid Intelligence System Based on Relevance Vector Machines and Imperialist Competitive Optimization for Modelling Forest Fire Danger Using GIS Partial discharge localisation methodology for power transformers based on improved acoustic propagation route search algorithm Using Imperial Competitive Algorithm for Solving Traveling Salesman Problem and Comparing the Efficiency of the Proposed Algorithm with Methods in Use EXPLICA: An Explorative Imperialist Competitive Algorithm based on the notion of Explorers with an expansive retention policy ACCURATE PREDICTION OF DGPS CORRECTION USING NEURAL NETWORK TRAINED BY IMPERIALISTIC COMPETITION ALGORITHM A new strategy for harmonic minimization based on triple switching of multilevel converters Comparative Study of Different Metaheuristics for the Trajectory Planning of a Robotic Arm Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm Virtual machine consolidated placement based on multi-objective biogeography-based optimization Equilibrium species counts and migration model tradeoffs for biogeography-based optimization An improving of migration operator in Biogeography-Based Optimization for solving Traveling Salesman Problem Scheduling Independent Jobs on Computational Grid using Biogeography Based Optimization Algorithm for Makespan Reduction Hybrid dynamic scheduling model for flexible manufacturing system with machine availability and new job arrivals Biogeography Optimization Algorithm Based Next Web Page Prediction Using Web Log and Web Content Features Multivariable Controller Design for Unified Power Flow Controller Using Evolutionary Optimization Algorithms The directed profitable rural postman problem with incompatibility constraints A matheuristic approach to the orienteering problem with service time dependent profits Optimised scheduling in human–robot collaboration – a use case in the assembly of printed circuit boards Meta-heuristics for the multiple trip vehicle routing problem with backhauls Advisory functions at European level Transformation and Analysis of Spatio-Temporal Statistical Linked Open Data with ESTA-LD Threshold adjustment control receiver and transmitter power to a system of communication atm. Chinese research and application of expert system shell ESTA Application of Information Technology for the Expert System Shell of Alfalfa in the New Lands Exploratory spatio-temporal analysis of linked statistical data Software Process Maturity and Capability Evaluation and its Implementation in the areas of E-Learning Organizations An event driven decision support algorithm for command and control of UAV fleets Frequency Domain Analysis of the Extended Super-Twisting Algorithm Two paths to a suboptimal solution – once more about optimality in reserve selection Constructing Service Function Chain Test Database: An Optimal Modeling Approach for Coordinated Resource Allocation Optimization of Vehicle Routing with Pickup Based on Multibatch Production Modelling and solving train scheduling problems under capacity constraints Offline Learning for Selection Hyper-heuristics with Elman Networks An analysis of heuristic subsequences for offline hyper-heuristic learning A HEURISTIC MODEL OF PARTIAL INTERDICTION MEDIAN PROBLEM FOR MULTI-SOURCING SUPPLY SYSTEMS Similarity-based Learning via Data Driven Embeddings A Novel Heuristic Auditor for Revealing Strong Consistency Violations in Cloud Further evidence for the memory state heuristic: Recognition latency predictions for binary inferences Abstract (1996): Learning as action: A social science approach to the evaluation of interactive media A Multi-heuristic Algorithmic Skeleton for Hard Combinatorial Optimization Problems A Bio-Inspired Deployment Method for Data Collection Networks in Wide White Areas Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Recurrent Pixel Embedding for Instance Grouping Factoring Variations in Natural Images with Deep Gaussian Mixture Models Neural Natural Language Inference Models Enhanced with External Knowledge Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks
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2012 2010 2005 2017 2013 2016 2018 2019 2005 2004 2005 2004 2013 2019 2018 2019 2016 2017 2016 2018 2017 2017 2018 2019 2018 2018 2011 2017 2015 2013 2016 2013 2016 2009 2016 2014 2015 2020 2017 2017 2019 2018 2016 1999 2016 1992 2006 2018 2016 2014 2009 2018 2008 2018 2018 2008 2017 2019 2014 2011 2017 2017 1996 2009 2018 2017 2018 2014 2018 2018 2016 2003
INFOCOM MCC@SIGCOMM ICPR GLOBECOM Workshops SMC (5) Asian Test Symposium EMNLP ICML ArXiv NeurIPS NIPS 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) EMNLP CDC ACC VSS Artificial Evolution CSO (1) PIMRC ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS ACL ACL ArXiv
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