Genetic Algorithms + Data Structures = Evolution Programs

Michalewicz, Z.,Michalewicz, Zbigniew

ISBN 10: 3540580905 / ISBN 13: 9783540580904

Editore: Springer-Verlag Telos, 1994

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Codice articolo S_450476301

Dati bibliografici

• Titolo: Genetic Algorithms + Data Structures = Evolution Programs
• Editore: Springer-Verlag Telos
• Data di pubblicazione: 1994
• Lingua: Inglese
• ISBN 10: 3540580905
• ISBN 13: 9783540580904
• Rilegatura: hardcover
• Edizione: seconda edizione
• Condizione: Very Good

Descrizione articolo

Riassunto

Genetic algorithms are based on the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, using genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the travelling salesman problem, and problems of scheduling, partitioning, and control. For the expanding area of parallel computation these techniques are becoming more and more important. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This second edition includes several new sections and many references to recent developments. A simple example of genetic code and an index are also added. Writing an evolution program for a given problem should be an enjoyable experience - this book may serve as a guide in this task.

Dalla quarta di copertina

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 has been growing in the last decade, 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. It is aimed at researchers, practitioners, and graduate students in computer science and artificial intelligence, operations research, and engineering.
This second edition includes several new sections and many references to recent developments. A simple example of genetic code and an index are also added. Writing an evolution program for a given problem should be an enjoyable experience - this book may serve as a guide to this task.