Phylogenetic Differential Evolution

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Author(s): Vinícius Veloso de Melo (University of São Paulo, Brazil), Danilo Vasconcellos Vargas (University of São Paulo, Brazil)and Marcio Kassouf Crocomo (University of São Paulo, Brazil)
Copyright: 2014
Pages: 19
Source title: Natural Computing for Simulation and Knowledge Discovery
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-4253-9.ch002

Keywords: Biologically-Inspired Computing / Biotechnology and Bioinformatics / Engineering Science Reference / Medicine & Healthcare

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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.

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