Phylogenetic Differential Evolution

Vinícius Veloso de Melo, Danilo Vasconcellos Vargas, Marcio Kassouf Crocomo

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationNatural Computing for Simulation and Knowledge Discovery
PublisherIGI Global
Pages22-40
Number of pages19
ISBN (Electronic)9781466642553
ISBN (Print)9781466642539
DOIs
Publication statusPublished - Jul 31 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Computer Science(all)
  • Chemistry(all)

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  • Cite this

    de Melo, V. V., Vargas, D. V., & Crocomo, M. K. (2013). Phylogenetic Differential Evolution. In Natural Computing for Simulation and Knowledge Discovery (pp. 22-40). IGI Global. https://doi.org/10.4018/978-1-4666-4253-9.ch002