Evolutionary Computation
Book Details
Format
Hardback or Cased Book
ISBN-10
0849305888
ISBN-13
9780849305887
Publisher
Taylor & Francis Inc
Imprint
CRC Press Inc
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 22nd, 2000
Print length
420 Pages
Weight
743 grams
Product Classification:
Algorithms & data structuresMachine learning
Ksh 47,700.00
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Offers the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. This title includes detailed coverage of binary and real encoding, and optimization applications.
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.
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.
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