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Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.
List of contents
FITNESS EVALUATION
Introduction to fitness evaluation
Encoding and decoding functions
Competitive fitness evaluation
Complexity-based fitness evaluation
Multiobjective optimization
CONSTRAINT-HANDLING TECHNIQUES
Introduction to constraint-handling techniques
Penalty functions
Decoders
Repair algorithms
Constraint-preserving operators
Other constraint-handling methods
Constraint-satisfaction problems
POPULATION STRUCTURES
Niching methods
Speciation methods
Island (migration) models: evolutionary algorithms based on punctuated equilibria
Diffusion (cellular) models
ADVANCED TECHNIQUES IN EVOLUTIONARY COMPUTATION
Population sizing
Mutation parameters
Recombination parameters
Parameter control
Self-adaptation
Meta-evolutionary approaches
Coevolutionary algorithms
IMPLEMENTATION OF EVOLUTIONARY ALGORITHMS
Efficient implementation of algorithms
Computation time of evolutionary operators
Hardware realizations of evolutionary algorithms
INDEX
About the author
Thomas Baeck
Summary
Describes basic ideas underlying evolutionary algorithms. This book covers such topics as fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is suitable for students of evolutionary computation.
Additional text
"This second volume provides very interesting information and updated material on additional topics. It also covers some advanced techniques and discusses the efficient implementation of evolutionary algorithms." -H.D. Hecker, Zentralblatt Math
