Evolutionary Computation: Theory and Applications
Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
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3D version adaptive application domain approach architecture artificial agents Artificial Intelligence automatically defined functions behavior biological CAM-Brain CAM8 cells circuits classifier system Colombetti complex constraints convergence cooperation credit assignment crossover D-E-A-D box database Dorigo EC system environment evaluated evolution strategies evolutionary algorithms evolutionary computation evolutionary programming evolved example experiments fault coverages Figure fitness values genetic algorithms genetic programming genetics-based global heuristics hybrid EP IEEE implemented improved individual initial input insect knowledge-lean Koza LCSs learning system machine learning method modular Morgan Kaufmann motif mutation neural networks neurons offspring operator optimization problems payoff penalty parameter performance population position possible prisoner's dilemma probability problem solver Proc protein random reinforcement learning residues robot Schmidhuber second phase selection sensors sequence signal simulated simulated annealing solution solve SSMs subdomains superoxide dismutase SWISS-PROT tion TPEP unsupervised learning vectors virus