Genetic Algorithms + Data Structures = Evolution Programs
Table of contents
About this book
Introduction
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.
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.
Keywords
Optimierung Suchalgorithmen algorithms data structures evolutionary computation genetic algorithms genetic programming heuristics learning machine learning optimization probabilistic algorithms probabilistische Algorithmen programming search techniques
Bibliographic information
- DOI https://doi.org/10.1007/978-3-662-03315-9
- Copyright Information Springer-Verlag Berlin Heidelberg 1996
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Springer Book Archive
- Print ISBN 978-3-642-08233-7
- Online ISBN 978-3-662-03315-9
- Buy this book on publisher's site