Overview
- Key source for studying and designing cellular GAs, as well as a self-contained primary reference book for these algorithms
- Throughout the book, there is an equal and parallel emphasis on both theory and practice
- Covers and provides results for both continuous and discrete problems - hence it's theoretical and application coverage is broad
- Explores both academic as well as real world problems, providing balance for researchers and practitioners
- Coverage includes multi-objective optimization, memetic extensions, and the relationship to new algorithms like EDAs, and high-interest-practical applications
- Includes supplementary material: sn.pub/extras
Part of the book series: Operations Research/Computer Science Interfaces Series (ORCS, volume 42)
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Table of contents (15 chapters)
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Front Matter
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Introduction
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Front Matter
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Characterizing Cellular Genetic Algorithms
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Front Matter
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Part II Characterizing Cellular Genetic Algorithms
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Algorithmic Models and Extensions
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Front Matter
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Part III Algorithmic Models and Extensions
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Applications of cGAs
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Front Matter
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Keywords
About this book
Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability.
The methods are benchmarked against well-known metaheuristics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of “vehicle routing” and the hot topics of “ad-hoc mobile networks” and “DNA genome sequencing” to clearly illustrate and demonstrate the power and utility of these algorithms.
Bibliographic Information
Book Title: Cellular Genetic Algorithms
Authors: Bernabe Dorronsoro, Enrique Alba
Series Title: Operations Research/Computer Science Interfaces Series
DOI: https://doi.org/10.1007/978-0-387-77610-1
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag US 2008
Hardcover ISBN: 978-0-387-77609-5Published: 06 June 2008
Softcover ISBN: 978-1-4419-4594-5Published: 08 December 2010
eBook ISBN: 978-0-387-77610-1Published: 05 April 2009
Series ISSN: 1387-666X
Series E-ISSN: 2698-5489
Edition Number: 1
Number of Pages: XIV, 248
Number of Illustrations: 72 b/w illustrations
Topics: Numerical Analysis, Operations Research/Decision Theory, Genetics and Population Dynamics, Algorithms, Operations Management, Optimization