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Title: Dynamic Selection of Evolutionary Operators Based on Online Learning and Fitness Landscape Analysis
Authors: Consoli, P. A.
Mei, Y.
Minku, Leandro Lei
Yao, X.
First Published: 5-Apr-2016
Citation: Soft Computing
Abstract: 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.
DOI Link: 10.1007/s00500-016-2126-x
ISSN: 1432-7643
eISSN: 1433-7479
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016, Springer. This article is published with open access at
Appears in Collections:Published Articles, Dept. of Computer Science

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