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Genetic algorithms + data structures = evolution programs / Zbigniew Michalewicz.

By: Michalewicz, Zbigniew.
Material type: TextTextPublisher: Berlin ; New York : Springer-Verlag, c1996Edition: 3rd rev. and extended ed.Description: xx, 387 p. : ill. ; 24 cm.ISBN: 3540606769 (hardcover); 9783540606765.Other title: Genetic algorithms plus data structures equals evolution programs.Subject(s): Evolutionary programming (Computer science) | Genetic algorithms | Data structures (Computer science)DDC classification: 005.1 Online resources: Publisher description | Table of contents only | WorldCat details
Contents:
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.
Summary: "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 aim of this book is to talk about the field of evolutionary computation in simple terms, and discuss the simplicity and elegance of its methods on many interesting test cases. The book may serve as a guide to writing an evolution program, and to making this an enjoyable experience. It is self-contained and the only prerequisite is basic undergraduate mathematics. Aimed at researchers, practitioners, and graduate students, it may serve as a text for advanced courses in computer science and artificial intelligence, operations research, and engineering." "This third edition has been substantially revised and extended. Three new chapters discuss the recent paradigm of genetic programming, heuristic methods and constraint handling, and current directions of research. Additional appendices contain test functions for experiments with evolutionary techniques and discuss possible projects for use in a project-oriented course."--BOOK JACKET.
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Text Text EWU Library
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Non-fiction 005.1 MIG 1996 (Browse shelf) C-1 Not For Loan 16323
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Text Text EWU Library
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Text Text EWU Library
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Text Text EWU Library
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Includes bibliographical references (p. [363]-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.


"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 aim of this book is to talk about the field of evolutionary computation in simple terms, and discuss the simplicity and elegance of its methods on many interesting test cases. The book may serve as a guide to writing an evolution program, and to making this an enjoyable experience. It is self-contained and the only prerequisite is basic undergraduate mathematics. Aimed at researchers, practitioners, and graduate students, it may serve as a text for advanced courses in computer science and artificial intelligence, operations research, and engineering." "This third edition has been substantially revised and extended. Three new chapters discuss the recent paradigm of genetic programming, heuristic methods and constraint handling, and current directions of research. Additional appendices contain test functions for experiments with evolutionary techniques and discuss possible projects for use in a project-oriented course."--BOOK JACKET.

Computer Science & Engineering

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