Exploitation of Linkage Learning in Evol
β Ying-ping Chen (ed.)
π Library
π
2010
π Springer
π English
β¦ LIBER β¦
π
Exploitation of Linkage Learning in Evolutionary Algorithms
β Scribed by Susan Khor (auth.), Ying-ping Chen (eds.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2010
- Tongue
- English
- Leaves
- 255
- Series
- Evolutionary Learning and Optimization 3
- Edition
- 1
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
β¦ Table of Contents
Front Matter....Pages -
Front Matter....Pages 1-1
Linkage Structure and Genetic Evolutionary Algorithms....Pages 3-23
Fragment as a Small Evidence of the Building Blocks Existence....Pages 25-44
Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm....Pages 45-69
DEUM β A Fully Multivariate EDA Based on Markov Networks....Pages 71-93
Front Matter....Pages 95-95
Pairwise Interactions Induced Probabilistic Model Building....Pages 97-122
ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information....Pages 123-137
Estimation of Distribution Algorithm Based on Copula Theory....Pages 139-162
Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks....Pages 163-189
Front Matter....Pages 191-191
Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA....Pages 193-214
Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics....Pages 215-226
Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method....Pages 227-241
Back Matter....Pages -
β¦ Subjects
Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Applications of Mathematics
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