Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Goodness-of-fit tests for copulas
Journal of Multivariate Analysis
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Estimation of distribution algorithm based on archimedean copulas
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Estimation of distribution algorithm based on copula theory
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Using Copulas in Estimation of Distribution Algorithms
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Pair-copula estimation of distribution algorithms
International Journal of Computing Science and Mathematics
International Journal of Computing Science and Mathematics
Chaotic differential evolution algorithm for resource constrained project scheduling problem
International Journal of Computing Science and Mathematics
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In order to solve the copula selection problem, an innovative method, called switched copula selection method is introduced, which is inspired by the mixed copula selection method. The new method can ignore how to determine a fixed copula beforehand, whereas it organises different kinds of copulas through a copula selection pool. Applying copula selection methods to estimation of distribution algorithms EDAs, the procedures of proposed mixed copula-EDAs and switched copula-EDAs are described in detail. Comparing with the single copula-EDAs, the experiment results show that switched copula-EDAs is easy to implement and provides a balance between accuracy and efficiency of the copula selection, in the meantime it promotes the performance whereas the computation complexity is almost the same.