Overview
- Advances our understanding of the linkage learning genetic algorithm and demonstrates potential research directions
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 190)
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About this book
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.
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Bibliographic Information
Book Title: Extending the Scalability of Linkage Learning Genetic Algorithms
Book Subtitle: Theory & Practice
Authors: Ying-ping Chen
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/b102053
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2006
Hardcover ISBN: 978-3-540-28459-8Published: 06 October 2005
Softcover ISBN: 978-3-642-06671-9Published: 19 November 2010
eBook ISBN: 978-3-540-32413-3Published: 16 September 2005
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XX, 120
Topics: Artificial Intelligence, Mathematical and Computational Engineering, Genetics and Population Dynamics, Bioinformatics, Biotechnology