Nonconvex Functions Optimization Using an Estimation of Distribution Algorithm Based on a Multivariate Extension of the Clayton Copula

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• 作者：Harold D. de Mello Jr. (18)
  André V. Abs da Cruz (18)
  Marley M. B. R. Vellasco (18)
  
• 关键词：Continuous numeric optimization ; evolutionary computation ; estimation of distribution algorithms ; copulas
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8669
• 期：1
• 页码：318-326
• 全文大小：360 KB
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  11. Guo, X., Wang, L., Zeng, J., Zhang, X.: VQ Codebook Design Algorithm Based on Copula Estimation of Distribution Algorithm. In: First International Conference on Robot, Vision and Signal Processing, pp. 178-81 (2011)
  12. DelaOssa, L., Gámez, J.A., Mateo, J.L., Puerta, J.M.: Avoiding premature convergence in estimation of distribution algorithms. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, pp. 455-62 (2009)
  
• 作者单位：Harold D. de Mello Jr. (18)
  André V. Abs da Cruz (18)
  Marley M. B. R. Vellasco (18)
  
  18. Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
  
• ISSN：1611-3349

文摘

This paper presents a copula-based estimation of a distribution algorithm with parameter updating for numeric optimization problems. This model implements an estimation of a distribution algorithm using a multivariate extension of Clayton’s bivariate copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional mutation and elitism operators jointly with a heuristic for a population restarting in the evolutionary process. We show that these approaches improve the overall performance of the optimization compared to other copula-based EDAs.