Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation, 2)

__Estimation of Distribution Algorithms: A New Tool for Evolutionary____Computation__ is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. __Estimation of Distribution Algorithms: A New____Tool for Evolutionary Computation__ is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. __Estimation of Distribution Algorithms: A New Tool for Evolutionary____Computation__ is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `__... I urge those who are interested in EDAs to study this____well-crafted book today.'__ David E. Goldberg, University of Illinois Champaign-Urbana. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited.
This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NNclassifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks.
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science.
'... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. ` ... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana. Front Matter....Pages i-xxxiv Front Matter....Pages 3-3 An Introduction to Evolutionary Algorithms....Pages 3-25 An Introduction to Probabilistic Graphical Models....Pages 27-56 A Review on Estimation of Distribution Algorithms....Pages 57-100 Benefits of Data Clustering in Multimodal Function Optimization via EDAs....Pages 101-127 Parallel Estimation of Distribution Algorithms....Pages 129-145 Mathematical Modeling of Discrete Estimation of Distribution Algorithms....Pages 147-163 Front Matter....Pages 165-165 An Empirical Comparison of Discrete Estimation of Distribution Algorithms....Pages 167-180 Experimental Results in Function Optimization with EDAs in Continuous Domain....Pages 181-194 Solving the 0-1 Knapsack Problem with EDAs....Pages 195-209 Solving the Traveling Salesman Problem with EDAs....Pages 211-229 Estimation of Distribution Algorithms Applied to the Job Shop Scheduling Problem: Some Preliminary Research....Pages 231-242 Solving Graph Matching with EDAs Using a Permutation-Based Representation....Pages 243-265 Front Matter....Pages 267-267 Feature Subset Selection by Estimation of Distribution Algorithms....Pages 269-293 Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms....Pages 295-311 Rule Induction by Estimation of Distribution Algorithms....Pages 313-322 Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs....Pages 323-341 An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering....Pages 343-360 Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms....Pages 361-377 Back Matter....Pages 379-382