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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.
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Previews available in: Polish English
Subjects
Computer algorithms, Computer programs, Data structures (Computer science), Evolutionary programming (Computer science), Genetic algorithms, Logiciels, Optimisation numérique, Saisie des données (Informatique), Problème transport, Algorithmes, Algorithme génétique, Partitionnement, Genetischer Algorithmus, Computadoras digitales (Programación), Structures de données (Informatique), Algorithmique, Estructura de datos (Ciencia de la computación), Ordonnancement, Voyageur commerce, Structure donnée, Programowanie ewolucyjne, Algorytmy genetyczne, Optymalizacja, Struktury danych (informatyka), Numerical analysis, Algorithms, Artificial intelligence, Software engineering, Operations research, Computer science, Qa76.9.a43 m53 1994, 005.1Edition | Availability |
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1
Genetic Algorithms + Data Structures = Evolution Programs
Mar 12, 2014, Springer
paperback
3662074192 9783662074190
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2
Genetic Algorithms + Data Structures = Evolution Programs
2013, Springer London, Limited
in English
3662028301 9783662028308
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3
Genetic Algorithms + Data Structures = Evolution Programs
2013, Springer London, Limited
in English
3662033151 9783662033159
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4
Genetic Algorithms + Data Structures = Evolution Programs
Sep 22, 2011, Springer
paperback
3642082335 9783642082337
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5
Algorytmy genetyczne + struktury danych = programy ewolucyjne
2003, Wydawnictwa Naukowo-Techniczne
in Polish
- Wyd. 3.
8320428815 9788320428810
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6
Genetic algorithms + data structures = evolution programs
1996, Springer-Verlag
in English
- 3rd rev. and extended ed.
3540606769 9783540606765
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aaaa
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7
Genetic Algorithms + Data Structures =: Evolution Programs
1994, Springer Berlin Heidelberg
electronic resource /
in English
- Second, Extended Edition.
3662074206 9783662074206
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8
Genetic algorithms + data structures = evolution programs
1994, Springer-Verlag
in English
- 2nd, extended ed.
3540580905 9783540580904
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zzzz
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9
Genetic algorithms + data structures = evolution programs
1992, Springer-Verlag
in English
3540553878 9783540553878
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Book Details
Edition Notes
Includes bibliographical references (p. [363]-382) and index.
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Work Description
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.
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