Evolutionary Computation

Evolutionary computing uses genetic algorithms to solve problems through a learning process. Each cycle of the application builds on information learned in its previous run, therefore its problem-solving evolves. In this book, the authors describe the basic principles of evolutionary computing, genetic algorithms, programming, and applications. Detailed coverage of binary and real encoding, including selection, crossover, and mutation, is included in two chapters. Discussion of evolution strategies covers strategy principles, mutations, recombination, and optimization. Applications for evolutionary computing are varied. Some of those covered in this book include: decision support, training & design of neural networks, pattern recognition, genetic programming, and cellular automata. TOC:Introduction.- Search, Optimization, Learning.- General Principles of Evolutive Algorithms.- Structure of a Genetic Algorithm.- Binary Encoding.- Usual Genetic Operators.- Real Encoding.- Optimization of Real Valued Functions.- Schemata Theory and Connected Problems.- Other Approaches concerning Genetic Algorithms.- Evolution Strategies.- Evolutionary Programming.- Applications.