Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice - Studies in Fuzziness and Soft Computing - Ying-ping Chen - 書籍 - Springer-Verlag Berlin and Heidelberg Gm - 9783642066719 - 2010年11月19日
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Ying-ping Chen
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice - Studies in Fuzziness and Soft Computing 1st Ed. Softcover of Orig. Ed. 2006 edition

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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.

142 pages, 4 black & white tables, biography

メディア 書籍     Paperback Book   (ソフトカバーで背表紙を接着した本)
リリース済み 2010年11月19日
ISBN13 9783642066719
出版社 Springer-Verlag Berlin and Heidelberg Gm
ページ数 142
寸法 156 × 234 × 7 mm   ·   208 g
言語 英語  

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