Genetic Algorithms + Data Structures = Evolution Programs
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
eBook, English, 1996
Third, revised and Extended edition View all formats and editions
Springer Berlin Heidelberg, Berlin, Heidelberg, 1996
1 online resource (xx, 387 pages)
9783662033159, 3662033151
851375253
Print version:
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