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Extending the Scalability of Linkage Learning Genetic Algorithms - Theory & Practice (Hardcover, 2006 ed.) Loot Price: R2,951
Discovery Miles 29 510
Extending the Scalability of Linkage Learning Genetic Algorithms - Theory & Practice (Hardcover, 2006 ed.): Ying-Ping Chen

Extending the Scalability of Linkage Learning Genetic Algorithms - Theory & Practice (Hardcover, 2006 ed.)

Ying-Ping Chen

Series: Studies in Fuzziness and Soft Computing, 190

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Loot Price R2,951 Discovery Miles 29 510 | Repayment Terms: R277 pm x 12*

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Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. However, most GAs employed in practice nowadays are unable to learn genetic linkage and suffer from the linkage problem. The linkage learning genetic algorithm (LLGA) was proposed to tackle the linkage problem with several specially designed mechanisms. While the LLGA performs much better on badly scaled problems than simple GAs, it does not work well on uniformly scaled problems as other competent GAs. Therefore, we need to understand why it is so and need to know how to design a better LLGA or whether there are certain limits of such a linkage learning process. This book aims to gain better understanding of the LLGA in theory and to improve the LLGA's performance in practice. It starts with a survey of the existing genetic linkage learning techniques and describes the steps and approaches taken to tackle the research topics, including using promoters, developing the convergence time model, and adopting subchromosomes.

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Studies in Fuzziness and Soft Computing, 190
Release date: October 2005
First published: 2006
Authors: Ying-Ping Chen
Dimensions: 235 x 155 x 9mm (L x W x T)
Format: Hardcover
Pages: 120
Edition: 2006 ed.
ISBN-13: 978-3-540-28459-8
Categories: Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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LSN: 3-540-28459-1
Barcode: 9783540284598

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