Exploitation of linkage learning in evolutionary algorithms
著者
書誌事項
Exploitation of linkage learning in evolutionary algorithms
（Adaptation, learning, and optimization / series editors in chief, Meng-Hiot Lin, Yew-Soon Ong, v. 3）
Springer, c2010
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内容説明・目次
内容説明
One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
目次
Linkage and Problem Structures.- Linkage Structure and Genetic Evolutionary Algorithms.- Fragment as a Small Evidence of the Building Blocks Existence.- Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm.- DEUM - A Fully Multivariate EDA Based on Markov Networks.- Model Building and Exploiting.- Pairwise Interactions Induced Probabilistic Model Building.- ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information.- Estimation of Distribution Algorithm Based on Copula Theory.- Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks.- Applications.- Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA.- Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics.- Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method.
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