chapter 1 Principles of evolutionary computation -- chapter 2 Genetic algorithms -- chapter 3 Basic selection schemes in evolutionary algorithms -- chapter 4 Selection based on scaling and ranking mechanisms -- chapter 5 Further selection strategies -- chapter 6 Recombination operators within binary encoding -- chapter 7 Mutation operators and related topics -- chapter 8 Schema theorem, building blocks, and related topics -- chapter 9 Real-valued encoding -- chapter 10 Hybridization, parametersetting, and adaptation -- chapter 11 Adaptive representations: messy genetic algorithms, delta coding, and diploidic representation -- chapter 12 Evolution strategies and evolutionary programming -- chapter 13 Population models and parallel implementations -- chapter 14 Genetic programming -- chapter 15 Learning classifier systems -- chapter 16 Applications of evolutionary computation.

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.