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Published in: Soft Computing 6/2016

19-03-2015 | Methodologies and Application

Bolstering efficient SSGAs based on an ensemble of probabilistic variable-wise crossover strategies

Authors: Jeonghwan Gwak, Moongu Jeon, Witold Pedrycz

Published in: Soft Computing | Issue 6/2016

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Abstract

A crossover operator in genetic algorithms (GAs) plays an essential role as the main search operator to breed offspring by exchanging information between individuals. Although different types of crossover operators have been developed for real-coded GAs (RCGAs), there has been very little research on combining different crossover operators to build more effective and efficient RCGAs. In this work, we propose new steady-state generation alternation-based RCGAs (SSGAs) ameliorated with (i) an ensemble of different probabilistic variable-wise crossover strategies, which is realized by the corresponding parallel populations, to utilize synergetic and complementary effect with their efficient operations, and (ii) efficient operation at each evolution step to obtain further performance enhancement. To investigate the performance of this ensemble with respect to search abilities and computation time, we compare the proposed algorithms against various SSGAs when running 27 benchmark functions. Empirical studies showed that the proposed algorithms exhibit better performance than the contestant SSGAs on these functions. Moreover, a comparison with the state-of-the-art evolutionary algorithms on eight difficult benchmark functions clearly demonstrated outperformance of the proposed algorithms.

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Appendix
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Metadata
Title
Bolstering efficient SSGAs based on an ensemble of probabilistic variable-wise crossover strategies
Authors
Jeonghwan Gwak
Moongu Jeon
Witold Pedrycz
Publication date
19-03-2015
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 6/2016
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1630-8

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