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Dynamic Selection of Evolutionary Operators Based on Online Learning and Fitness Landscape Analysis

journal contribution
posted on 2016-04-07, 10:51 authored by P. A. Consoli, Y. Mei, Leandro Lei Minku, X. Yao
Self-adaptive mechanisms for the identification of the most suitable variation operator in evolutionary algorithms rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel adaptive operator selection mechanism which uses a set of four fitness landscape analysis techniques and an online learning algorithm, dynamic weighted majority, to provide more detailed information about the search space to better determine the most suitable crossover operator. Experimental analysis on the capacitated arc routing problem has demonstrated that different crossover operators behave differently during the search process, and selecting the proper one adaptively can lead to more promising results.

History

Citation

Soft Computing

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Version

  • VoR (Version of Record)

Published in

Soft Computing

issn

1432-7643

eissn

1433-7479

Acceptance date

2016-03-25

Copyright date

2016

Available date

2016-04-07

Publisher version

http://link.springer.com/article/10.1007/s00500-016-2126-x

Language

en

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