Estimation of distribution algorithm based on copula theory

  • Authors:

  • Li-Fang Wang;Jian-Chao Zeng;Yi Hong

  • Affiliations:

  • Complex System and Computational Intelligence Laboratory, Taiyuan University of Science & Technology, Taiyuan, China and College of Electrical and Information Engineering, Lanzhou University o ...;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science & Technology, Taiyuan, China;College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

Estimation of Distribution Algorithm (EDA) is a novel evolutionary computation, which mainly depends on learning and sampling mechanisms to manipulate the evolutionary search, and has been proved a potential technique for complex problems. However, EDA generally spend too much time on the learning about the probability distribution of the promising individuals. The paper propose an improved EDA based on copula theory (copula-EDA) to enhance the learning efficiency, which models and samples the joint probability function by selecting a proper copula and learning the marginal probability distributions of the promising population. The simulating results prove the approach is easy to implement and is validated on several problems.