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Phylogenetic Differential Evolution

Phylogenetic Differential Evolution

Vinícius Veloso de Melo, Danilo Vasconcellos Vargas, Marcio Kassouf Crocomo
Copyright: © 2014 |Pages: 19
ISBN13: 9781466642539|ISBN10: 146664253X|EISBN13: 9781466642546
DOI: 10.4018/978-1-4666-4253-9.ch002
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MLA

Veloso de Melo, Vinícius, et al. "Phylogenetic Differential Evolution." Natural Computing for Simulation and Knowledge Discovery, edited by Leandro Nunes de Castro, IGI Global, 2014, pp. 22-40. https://doi.org/10.4018/978-1-4666-4253-9.ch002

APA

Veloso de Melo, V., Vargas, D. V., & Crocomo, M. K. (2014). Phylogenetic Differential Evolution. In L. Nunes de Castro (Ed.), Natural Computing for Simulation and Knowledge Discovery (pp. 22-40). IGI Global. https://doi.org/10.4018/978-1-4666-4253-9.ch002

Chicago

Veloso de Melo, Vinícius, Danilo Vasconcellos Vargas, and Marcio Kassouf Crocomo. "Phylogenetic Differential Evolution." In Natural Computing for Simulation and Knowledge Discovery, edited by Leandro Nunes de Castro, 22-40. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4253-9.ch002

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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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