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.

Klappentext

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 Com

Inhalt

Principles of Evolutionary Computation
Genetic Algorithms
Basic Selection Schemes in Evolutionary Algorithms
Selection Based on Scaling and Ranking Mechanisms
Further Selection Strategies
Recombination Operators within Binary Encoding
Mutation and other Search Operators
Schema Theorem, Building Blocks and Related Topics
Real-valued Encoding
Hybridization, Parameter Setting and Adaptation
Adaptive Representations: Messy Genetic Algorithms, Delta Coding and Diploidic Representation
Evolution Strategies and Evolutionary Programming
Population Models and Parallel Implementations
Genetic Programming
Learning Classifier Systems
Applications of Evolutionary Computation