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Studies in Fuzziness and Soft Computing

Extending the Scalability of Linkage Learning Genetic Algorithms

Theory & Practice

Authors: Chen, Ying-ping

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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, unable to learn linkage among genes, most GAs employed in practice nowadays suffer from the linkage problem, which refers to the need of appropriately arranging or adaptively ordering the genes on chromosomes during the evolutionary process. These GAs require their users to possess prior domain knowledge of the problem such that the genes on chromosomes can be correctly arranged in advance. One way to alleviate the burden of GA users is to make the algorithm capable of adapting and learning genetic linkage by itself.

In order to tackle the linkage problem, the linkage learning genetic algorithm (LLGA) was proposed using a unique combination of the (gene number, allele) coding scheme and an exchange crossover to permit GAs to learn tight linkage of building blocks through a special probabilistic expression. 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 and classification 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. It also provides the experimental results for observation of the linkage learning process as well as for verification of the theoretical models proposed in this study.

Table of contents (9 chapters)

Buy this book

eBook $149.00
price for USA (gross)
  • ISBN 978-3-540-32413-3
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $189.00
price for USA
  • ISBN 978-3-540-28459-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $189.00
price for USA
  • ISBN 978-3-642-06671-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Extending the Scalability of Linkage Learning Genetic Algorithms
Book Subtitle
Theory & Practice
Authors
Series Title
Studies in Fuzziness and Soft Computing
Series Volume
190
Copyright
2006
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
The Editor(s) (if applicable) and The Author(s) 2018
eBook ISBN
978-3-540-32413-3
DOI
10.1007/b102053
Hardcover ISBN
978-3-540-28459-8
Softcover ISBN
978-3-642-06671-9
Series ISSN
1434-9922
Edition Number
1
Number of Pages
XX, 120
Topics