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A study on scale factor/crossover interaction in distributed differential evolution

A study on scale factor/crossover interaction in distributed differential evolution This paper studies the use of multiple scale factor values within distributed Differential Evolution structures employing the so-called exponential crossover. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed Differential Evolution structures and tested on several various test problems. The results are then compared to those of a previous study where the so-called binomial crossover was employed. Numerical results show that, when associated to the exponential crossover, the employment of multiple scale factors is not systematically beneficial and in some cases even detrimental to the performance of the algorithm. The exponential crossover accentuates the exploitative character of the Differential Evolution, which cannot always be counterbalanced by the increase in the explorative aspect of the algorithm introduced by the employment of multiple scale factor values. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

A study on scale factor/crossover interaction in distributed differential evolution

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References (22)

Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-011-9267-1
Publisher site
See Article on Publisher Site

Abstract

This paper studies the use of multiple scale factor values within distributed Differential Evolution structures employing the so-called exponential crossover. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed Differential Evolution structures and tested on several various test problems. The results are then compared to those of a previous study where the so-called binomial crossover was employed. Numerical results show that, when associated to the exponential crossover, the employment of multiple scale factors is not systematically beneficial and in some cases even detrimental to the performance of the algorithm. The exponential crossover accentuates the exploitative character of the Differential Evolution, which cannot always be counterbalanced by the increase in the explorative aspect of the algorithm introduced by the employment of multiple scale factor values.

Journal

Artificial Intelligence Review – Springer Journals

Published: Jun 8, 2011

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