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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a model new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by altering the crossover and mutation operators with learning and sampling from the prospect distribution of the simplest individuals of the inhabitants at each iteration of the algorithm. Working in such a way, the relationships between the variables involved within the concern space are explicitly and efficiently captured and exploited.
This textual content material constitutes the first compilation and evaluation of the strategies and functions of this new device for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Half I is dedicated to the foundations of EDAs. On this half, after introducing some probabilistic graphical fashions – Bayesian and Gaussian networks – a consider of current EDA approaches is launched, along with some new methods based mostly totally on additional versatile probabilistic graphical fashions. A mathematical modeling of discrete EDAs can be launched. Half II covers a quantity of functions of EDAs in some classical optimization points: the travelling salesman disadvantage, the job scheduling disadvantage, and the knapsack disadvantage. EDAs are moreover utilized to the optimization of some correctly-acknowledged combinatorial and regular options. Half III presents the equipment of EDAs to unravel some points that come up inside the machine learning space: perform subset selection, perform weighting in Okay-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimum weights in artificial neural networks.
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and fascinating device for researchers working inside the topic of evolutionary computation and for engineers who face precise-world optimization points. This book may also be utilized by graduate school college students and researchers in laptop science.
`… I urge these which might be contemplating EDAs to evaluation this correctly-crafted book proper now.' David E. Goldberg, School of Illinois Champaign-Urbana.
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