Extending the scalability of linkage learning genetic algorithms : theory & practice
著者
書誌事項
Extending the scalability of linkage learning genetic algorithms : theory & practice
（Studies in fuzziness and soft computing, v. 190）
Springer, c2006
- タイトル別名
-
Extending the scalability of linkage learning genetic algorithms : theory and practice
大学図書館所蔵 件 / 全7件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. [109]-115) and index
内容説明・目次
内容説明
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.
目次
Introduction.- Genetic Algorithms and Genetic Linkage.- Genetic Linkage Learning Techniques .- Linkage Learning Genetic Algorithm.- Preliminaries: Assumptions and the Test Problem.- A First Improvement: Using Promoters.- Convergence Time for the Linkage Learning Genetic Algorithm.-Introducing Subchromosome Representations.- Conclusions.
「Nielsen BookData」 より
