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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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
Online AccessGet full text
ISBN9781424437023
1424437024
ISSN2160-133X
DOI10.1109/ICMLC.2009.5212795

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Abstract 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.
AbstractList 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.
Author Qing-Sheng Ren
Jin Zeng
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Snippet 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...
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StartPage 3616
SubjectTerms 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
Title High-order EDA
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Volume 6
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