Evolutionary computation is the study of computational systems which use ideas and get inspirations from natural evolution and adaptation. This book is devoted to the theory and applications of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can be divided into four major parts: introduction, theory, evolutionary optimisation, and evolutionary learning. Each part consists of several chapters which present an in-depth discussion of selected topics. The emphasis of this book is on problem solving techniques. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection has enabled us to incorporate many good ideas in more established fields into evolutionary algorithms so that we are not reinventing wheels. The book is aimed at a wide range of readers. It does not require previous exposure to the field of evolutionary computation since introductory material is included. It should be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, operations research, and most engineering fields should find it of interest.
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
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Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, New York, San Mateo, Los Altos, Int'l Conf, John Wiley, San Francisco, Intl Conf, Generation Figure, World Scientific, Ann Arbor, Internal Critic, Lecture Notes, Test-Case Manager, Behavior Engineering, Int'l Joint Conf, Journal of Molecular Biology, Michigan Press, Parallel Problem Solving, University of Michigan, Data Structures, Menlo Park, Academic Press, Amos Bairoch New!
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, New York, San Mateo, Los Altos, Int'l Conf, John Wiley, San Francisco, Intl Conf, Generation Figure, World Scientific, Ann Arbor, Internal Critic, Lecture Notes, Test-Case Manager, Behavior Engineering, Int'l Joint Conf, Journal of Molecular Biology, Michigan Press, Parallel Problem Solving, University of Michigan, Data Structures, Menlo Park, Academic Press, Amos Bairoch New!
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
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This book cites 58
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6
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- Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg on 4 pages
- Artificial Neural Nets and Genetic Algorithms by Vera Kurkova on page 193, page 329, and page 330
- Machine Learning by Tom M. Mitchell on page 122, page 167, and page 169
- Genetic Algorithms and Simulated Annealing by Lawrence Davis on page 34, page 194, and Back Matter
- Evolutionary Computation by A.E. Eiben on page 28, and page 32
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- Engineering Self-Organising Systems: Third International Workshop, ESOA 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers ... / Lecture Notes in Artificial Intelligence) by Sven A. Brueckner on page 59
- Adaptive Computing in Design and Manufacture V (v. 5) by I.C. Parmee on page 50
- Engineering Self-Organising Systems: Methodologies and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) by Sven A. Brueckner on page 122
- Machine Learning: ECML'97: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) by Maarten van Someren on page 220
- Genetic Algorithms and Engineering Design by Mitsuo Gen in Back Matter
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