Synopses & Reviews
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
Includes bibliographical references (p. -382) and index.
Table of Contents
Introduction.- Part I. Genetic Algorithms. GAs: What Are They? - GAs: How Do They Work? - GAs: Why Do They Work? - GAs: Selected Topics.- Part II. Numerical Optimization. Binary or Float? - Fine Local Tuning.- Handling Constraints.- Evolution Strategies and Other Methods.- Part III. Evolution Programs. The Transportation Problem.- The Traveling Salesman Problem.- Machine Learning.- Evolutionary Programming and Genetic Programming.- A Hierarchy of Evolution Programs.- Evolution Programs and Heuristics.- Conclusions.- Appendices.- References.- Index.