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Phylogenetic Differential Evolution

Author

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  • Vinícius Veloso de Melo

    (University of São Paulo, Brazil)

  • Danilo Vasconcellos Vargas

    (University of São Paulo, Brazil)

  • Marcio Kassouf Crocomo

    (University of São Paulo, Brazil)

Abstract

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

Suggested Citation

  • Vinícius Veloso de Melo & Danilo Vasconcellos Vargas & Marcio Kassouf Crocomo, 2011. "Phylogenetic Differential Evolution," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 2(1), pages 21-38, January.
    • Handle: RePEc:igg:jncr00:v:2:y:2011:i:1:p:21-38
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