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Titlebook: Extending the Scalability of Linkage Learning Genetic Algorithms; Theory & Practice Ying-ping Chen Book 2006 Springer-Verlag Berlin Heidelb

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書(shū)目名稱(chēng)Extending the Scalability of Linkage Learning Genetic Algorithms
副標(biāo)題Theory & Practice
編輯Ying-ping Chen
視頻videohttp://file.papertrans.cn/320/319843/319843.mp4
概述Advances our understanding of the linkage learning genetic algorithm and demonstrates potential research directions.Includes supplementary material:
叢書(shū)名稱(chēng)Studies in Fuzziness and Soft Computing
圖書(shū)封面Titlebook: Extending the Scalability of Linkage Learning Genetic Algorithms; Theory & Practice Ying-ping Chen Book 2006 Springer-Verlag Berlin Heidelb
描述.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. .
出版日期Book 2006
關(guān)鍵詞Chromosome Representation; Genetic Algorithms; Genetic Linkage Learning Techniques; Soft Computing; algo
版次1
doihttps://doi.org/10.1007/b102053
isbn_softcover978-3-642-06671-9
isbn_ebook978-3-540-32413-3Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 2006
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Springer Series in Materials Scienceich refers to the process of building-block formation, was less successful on problems with uniformly scaled building blocks, and this chapter seeks to better understand why this was so and to correct the deficiency by adopting a coding mechanism, ., that exists in genetics.
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David M. Cwiertny,Michelle M. Schererm in theory. Particularly, a convergence time model is constructed to explain why the linkage learning genetic algorithm needs exponentially growing computational time to solve uniformly scaled problems [15].
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Introduction,main knowledge of the problem such that the genes on chromosomes can be correctly arranged in advance. One way to alleviate this burden of genetic algorithm users is to make the algorithm capable of adapting and learning genetic linkage by itself.
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Genetic Linkage Learning Techniques,hard problems quickly, accurately, and reliably. Such . genetic and evolutionary algorithms take the problems that were intractable for the first-generation genetic algorithms and render them practical in polynomial time (oftentimes, in subquadratic time) [32, 72–74]
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Convergence Time for the Linkage Learning Genetic Algorithm,m in theory. Particularly, a convergence time model is constructed to explain why the linkage learning genetic algorithm needs exponentially growing computational time to solve uniformly scaled problems [15].
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