Evolutionary ComputationRapid 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. |
Contents
Principles of evolutionary computation 1 | 7 |
Genetic algorithms | 21 |
Basic selection schemes | 39 |
Selection based on scaling | 57 |
Further selection strategies | 83 |
Recombination operators within | 103 |
Mutation operators and related topics | 131 |
Schema theorem building blocks | 153 |
References and bibliography | 184 |
Realvalued encoding | 187 |
Hybridization parameter setting | 213 |
Other editions - View all
Evolutionary Computation D. Dumitrescu,Beatrice Lazzerini,Lakhmi C. Jain,A. Dumitrescu Limited preview - 2000 |
Common terms and phrases
4th Int adaptive representation applications Bäck best individual binary encoding building blocks Cauchy Cauchy distribution cellular automaton chromosomes classifier systems Conf convergence crossover operator crossover points delta coding diploid Dumitrescu evaluation evolution strategies evolutionary algorithm Evolutionary Computation evolutionary programming evolve fitness function fitness value Fogel fuzzy rules gene genetic drift genetic programming genotype Goldberg IEEE introns L.B. Booker Eds learning classifier systems Let us consider Machine Learning mating mechanism messy genetic algorithms method Michalewicz models Morgan Kaufmann mutation operator mutation probability mutation rate netic algorithms neural network objective function obtained offspring optimum parallel parents perturbation phenotype population P(t position Proc procedure proportional selection random real-valued encoding recombination operator References and bibliography Remarks replacement represents rithms San Mateo schema theorem schemata Schwefel search operators search process search space selection operator selection pressure selection probability selection schemes self-adaptation Simulated Annealing solution solve strategy parameters string symbol tion variable vector