Towards a New Evolutionary Computation [electronic resource] : Advances in the Estimation of Distribution Algorithms / edited by Jose A. Lozano, Pedro LarraȘaga, IȘaki Inza, Endika Bengoetxea.

Linking Entropy to Estimation of Distribution Algorithms -- Entropy-based Convergence Measurement in Discrete Estimation of Distribution Algorithms -- Real-coded Bayesian Optimization Algorithm -- The CMA Evolution Strategy: A Comparing Review -- Estimation of Distribution Programming: EDA-based Approach to Program Generation -- Multi-objective Optimization with the Naive ID A -- A Parallel Island Model for Estimation of Distribution Algorithms -- GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm -- Bayesian Classifiers in Optimization: An EDA-like Approach -- Feature Ranking Using an EDA-based Wrapper Approach -- Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology -- Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem.

This is a nicely edited volume on Estimation of Distribution Algorithms (EDAs) by leading researchers on this important topic. It covers a wide range of topics in EDAs, from theoretical analysis to experimental studies, from single objective to multi-objective optimisation, and from parallel EDAs to hybrid EDAs. It is a very useful book for everyone who is interested in EDAs, evolutionary computation or optimisation in general. Xin Yao, IEEE Fellow Editor-in-Chief, IEEE Transactions on Evolutionary Computation ______________________________________________________________ Estimation of Distribution Algorithms (EDAs) have "removed genetics" from Evolutionary Algorithms (EAs). However, both approaches (still) have a lot in common, and, for instance, each one could be argued to in fact include the other! Nevertheless, whereas some theoretical approaches that are specific to EDAs are being proposed, many practical issues are common to both fields, and, though proposed in the mid 90's only, EDAs are catching up fast now with EAs, following many research directions that have proved successful for the latter: opening to different search domains, hybridizing with other methods (be they OR techniques or EAs themselves!), going parallel, tackling difficult application problems, and the like. This book proposes an up-to-date snapshot of this rapidly moving field, and witnesses its maturity. It should hence be read ... rapidly, by anyone interested in either EDAs or EAs, or more generally in stochastic optimization. Marc Schoenauer Editor-in-Chief, Evolutionary Computation

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