High-order EDA

In this paper, we investigate the usage of history information for estimation of distribution algorithm (EDA). In EDA, the distribution is estimated from a set of selected individuals and then the estimated distribution model is used to generate new individuals. It needs large population size to con...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3616 - 3621
Main Authors Jin Zeng, Qing-Sheng Ren
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Computer science
Constraint optimization
Convergence
Cybernetics
Electronic design automation and methodology
Estimation of distribution algorithm (EDA)
Filters
Genetic algorithms
High-order EDA
History
Machine learning
Mathematics
Optimization methods
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Summary:In this paper, we investigate the usage of history information for estimation of distribution algorithm (EDA). In EDA, the distribution is estimated from a set of selected individuals and then the estimated distribution model is used to generate new individuals. It needs large population size to converge to the global optimum. A new algorithm, the high-order EDA, is proposed based on the idea of filter. By the usage of history information, it can converge to the global optimum with high probability even with small population size. Convergence properties are then discussed. We also show the application for constrained optimization problems.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212795