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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice

Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice

Chen, Ying-Ping

Paperback

Series: Studies in Fuzziness and Soft Computing, Book 190

BiologyGeneral ComputersGeneral Mathematics

ISBN10: 3642066712
ISBN13: 9783642066719
Publisher: Springer Nature
Published: Nov 19 2010
Pages: 120
Weight: 0.46
Height: 0.30 Width: 6.14 Depth: 9.21
Language: English

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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