Effective Linkage Learning Using Low-Order Statistics and Clustering
- 30 October 2009
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Evolutionary Computation
- Vol. 13 (6), 1233-1246
- https://doi.org/10.1109/tevc.2009.2025455
Abstract
The adoption of probabilistic models for selected individuals is a powerful approach for evolutionary computation. Probabilistic models based on high-order statistics have been used by estimation of distribution algorithms (EDAs), resulting better effectiveness when searching for global optima for hard optimization problems. This paper proposes a new framework for evolutionary algorithms, which combines a simple EDA based on order 1 statistics and a clustering technique in order to avoid the high computational cost required by higher order EDAs. The algorithm uses clustering to group genotypically similar solutions, relying that different clusters focus on different substructures and the combination of information from different clusters effectively combines substructures. The combination mechanism uses an information gain measure when deciding which cluster is more informative for any given gene position, during a pairwise cluster combination. Empirical evaluations effectively cover a comprehensive range of benchmark optimization problems.Keywords
Other Versions
This publication has 15 references indexed in Scilit:
- An Incremental Approach for Niching and Building Block Detection via ClusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Exact Bayesian network learning in estimation of distribution algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Clustering-Based Probabilistic Model Fitting in Estimation of Distribution AlgorithmsIEICE Transactions on Information and Systems, 2006
- An Evolutionary Algorithm With Guided Mutation for the Maximum Clique ProblemIEEE Transactions on Evolutionary Computation, 2005
- On Stability of Fixed Points of Limit Models of Univariate Marginal Distribution Algorithm and Factorized Distribution AlgorithmIEEE Transactions on Evolutionary Computation, 2004
- Multiple-Deme Parallel Estimation of Distribution Algorithms: Basic Framework and ApplicationLecture Notes in Computer Science, 2004
- The compact genetic algorithmIEEE Transactions on Evolutionary Computation, 1999
- Schemata, Distributions and Graphical Models in Evolutionary OptimizationJournal of Heuristics, 1999
- Approximate Is Better than "Exact" for Interval Estimation of Binomial ProportionsThe American Statistician, 1998
- Removing the Genetics from the Standard Genetic AlgorithmPublished by Elsevier BV ,1995