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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) ReviewThis is a good book to learn about Estimation of Distribution Algorithms (EDAs or also called DEAs or Iterated DEAs). These algorithms are similar to evolutionary algorithms, but do not use the crossover or mutation operators of evolutionary search. EDAs instead create a probabilistic model of good solutions and use the model to generate new search points. It's a nifty idea and it works.Most of the chapters of this edited collection were authored or coauthored by the editors. So, algorithms developed by other people do not get a lot of attention. However, the editors (or is it the authors) manage to include chapters on combinatorial, continuous, and discrete optimization.
There is a section on machine learning applications that is OK, but the last chapter on training neural nets with EDAs is very weak (look ma I used this and it worked...). Except for this chapter, the rest of the chapters in this section use careful experiments and statistics to make their points.
Making the source code available would have improved things and would make it easier for people to try these algorithms.Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) OverviewEstimation of Distribution Algorithms: A New Tool forEvolutionary Computation is devoted to a new paradigm forevolutionary computation, named estimation of distribution algorithms(EDAs). This new class of algorithms generalizes genetic algorithms byreplacing the crossover and mutation operators with learning andsampling from the probability distribution of the best individuals ofthe population at each iteration of the algorithm. Working in such away, the relationships between the variables involved in the problemdomain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of thetechniques and applications of this new tool for performingevolutionary computation. Estimation of Distribution Algorithms: ANew Tool for Evolutionary Computation is clearly divided intothree parts. Part I is dedicated to the foundations of EDAs. In thispart, after introducing some probabilistic graphical models -Bayesian and Gaussian networks - a review of existing EDAapproaches is presented, as well as some new methods based on moreflexible probabilistic graphical models. A mathematical modeling ofdiscrete EDAs is also presented. Part II covers several applicationsof EDAs in some classical optimization problems: the travellingsalesman problem, the job scheduling problem, and the knapsackproblem. EDAs are also applied to the optimization of some well-knowncombinatorial and continuous functions. Part III presents theapplication of EDAs to solve some problems that arise in the machinelearning field: feature subset selection, feature weighting inK-NN classifiers, rule induction, partial abductive inferencein Bayesian networks, partitional clustering, and the search foroptimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool forEvolutionary Computation is a useful and interesting tool forresearchers working in the field of evolutionary computation and forengineers who face real-world optimization problems. This book mayalso be used by graduate students and researchers in computer science.`... I urge those who are interested in EDAs to study thiswell-crafted book today.' David E. Goldberg, University ofIllinois Champaign-Urbana.
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