Evolutionary Computation

Inbunden, Engelska, 2000

Del i serien International Series on Computational Intelligence

2 794 kr

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Beskrivning

Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation.Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.

Produktinformation

Utgivningsdatum:2000-06-22
Mått:156 x 234 x undefined mm
Vikt:743 g
Format:Inbunden
Språk:Engelska
Serie:International Series on Computational Intelligence
Antal sidor:420
Förlag:Taylor & Francis Inc
ISBN:9780849305887

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Programmeringsböcker inom Data och IT

Mer om författaren

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

Recensioner i media

"…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

Innehållsförteckning

Principles of Evolutionary ComputationGenetic AlgorithmsBasic Selection Schemes in Evolutionary AlgorithmsSelection Based on Scaling and Ranking MechanismsFurther Selection StrategiesRecombination Operators within Binary EncodingMutation and other Search OperatorsSchema Theorem, Building Blocks and Related TopicsReal-valued EncodingHybridization, Parameter Setting and AdaptationAdaptive Representations: Messy Genetic Algorithms, Delta Coding and Diploidic RepresentationEvolution Strategies and Evolutionary ProgrammingPopulation Models and Parallel ImplementationsGenetic ProgrammingLearning Classifier SystemsApplications of Evolutionary Computation