Author: Ying-ping Chen
Edition: 2006
Publisher: Springer
Binding: Hardcover
ISBN: 3540284591
Category: Programming
List Price: $ 139.00
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Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice (Studies in Fuzziness and Soft Computing)
Genetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied to solve problems in many disciplines. Programming books Extending the Scalability of Linkage Learning Genetic Algorithms pdf. 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. Download books Extending the Scalability of Linkage Learning Genetic Algorithms pdf via mediafire, 4shared, rapidshare.
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Extending the Scalability of Linkage Learning Genetic Algorithms: Ying-Ping Chen
Extending the Scalability of Linkage Learning Genetic Algorithms: Ying-Ping Chen
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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
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
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TheNile.com.au About FAQ Payment Delivery Contact Us 1800-987-323 Extending the Scalability of Linkage Learning Genetic Algorithms: Theory Practice (English) by Ying-Ping Chen Format Hardcover Condition Brand New 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. T
TheNile.com.au About FAQ Payment Delivery Contact Us 1800-987-323 Extending the Scalability of Linkage Learning Genetic Algorithms: Theory Practice (English) by Ying-Ping Chen Format Hardcover Condition Brand New 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. T
Price: $179.39
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice Springer 9783540284598 09783540284598
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice Springer 9783540284598 09783540284598
Price: $179.39
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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. Download free Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice (Studies in Fuzziness and Soft Computing) pdf
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