Overview

Evolutionary computing uses genetic algorithms to solve problems through a learning process. Each cycle of the application builds on information learned in its previous run, therefore its problem-solving ""evolves"". In this book, the authors describe the basic principles of evolutionary computing, genetic algorithms, programming, and applications. Detailed coverage of binary and real encoding, including selection, crossover, and mutation, is included in two chapters. Discussion of evolution strategies covers strategy principles, mutations, recombination, and optimization. Applications for evolutionary computing are varied. Some of those covered in this book include: decision support, training & design of neural networks, pattern recognition, genetic programming, and cellular automata.

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9780849305887

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""…offers a presentation of the main ideas, models, and algorithms of evolutionary computation. Anyone who is inspired by the work of L. Davis or D.E. Goldberg to use evolutionary computation as a tool in hos/her daily work will find a complete overview of techniques and strategies…Anyone interested in optimization or search problems can find useful ideas, methods, and algorithms."" - Journal of Chemometrics, 2002

offers a presentation of the main ideas, models, and algorithms of evolutionary computation. Anyone who is inspired by the work of L. Davis or D.E. Goldberg to use evolutionary computation as a tool in hos/her daily work will find a complete overview of techniques and strategiesAnyone interested in optimization or search problems can find useful ideas, methods, and algorithms. - Journal of Chemometrics, 2002

Author Information

Beatrice Lazzerini, D. Dumitrescu, Lakhmi C. Jain. A. Dumitrescu

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