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About this book
Efficient and Accurate Parallel Genetic Algorithms is about the design of parallel GAs. It presents theoretical developments that improve our understanding of the effect of the algorithm's parameters on its search for quality and efficiency. These developments are used to formulate guidelines on how to choose the parameter values that minimize the execution time while consistently reaching solutions of high quality.
Efficient and Accurate Parallel Genetic Algorithms can be read in several ways, depending on the readers' interests and their previous knowledge about these algorithms. Newcomers to the field will find the background material in each chapter useful to become acquainted with previous work, and to understand the problems that must be faced to design efficient and reliable algorithms. Potential users of parallel GAs that may have doubts about their practicality or reliability may be more confident after reading this book and understanding the algorithms better. Those who are ready to try a parallel GA on their applications may choose to skim through the background material, and use the results directly without following the derivations in detail. These readers will find that using the results can help them to choose the type of parallel GA that best suits their needs, without having to invest the time to implement and test various options. Once that is settled, even the most experienced users dread the long and frustrating experience of configuring their algorithms by trial and error. The guidelines contained herein will shorten dramatically the time spent tweaking the algorithm, although some experimentation may still be needed for fine-tuning.
Efficient and Accurate Parallel Genetic Algorithms is suitable as a secondary text for a graduate level course, and as a reference for researchers and practitioners in industry.
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Table of contents (9 chapters)
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David Goldberg
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Bibliographic Information
Book Title: Efficient and Accurate Parallel Genetic Algorithms
Authors: Erick Cantú-Paz
Series Title: Genetic Algorithms and Evolutionary Computation
DOI: https://doi.org/10.1007/978-1-4615-4369-5
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Kluwer Academic Publishers 2001
Hardcover ISBN: 978-0-7923-7221-9Published: 30 November 2000
Softcover ISBN: 978-1-4613-6964-6Published: 23 October 2012
eBook ISBN: 978-1-4615-4369-5Published: 06 December 2012
Series ISSN: 1568-2587
Edition Number: 1
Number of Pages: XVI, 162
Topics: Artificial Intelligence, Computer Science, general, Theory of Computation