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Competitive island - based cooperative coevolution for efficient optimization of large - scale fully - separable continuous functions

Bali, Kavitesh and Chandra, Rohitash and Omidvar, M.N. (2015) Competitive island - based cooperative coevolution for efficient optimization of large - scale fully - separable continuous functions. In: Neural Information Processing. Lecture Notes in Computer Science, 9491 . Springer International Publishing, Switzerland, pp. 137-147. ISBN 978-3-319-26554-4

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    Abstract

    In this paper, we investigate the performance of introducing competition in cooperative coevolutionary algorithms to solve large-scale fully-separable continuous optimization problems. It may seem that solving large-scale fully-separable functions is trivial by means of problem decomposition. In principle, due to lack of variable interaction in fully-separable problems, any decomposition is viable. However, the decomposition strategy has shown to have a significant impact on the performance of cooperative coevolution on such functions. Finding an optimal decomposition strategy for solving fully-separable functions is laborious and requires extensive empirical studies. In this paper, we use a competitive two-island cooperative coevolution in which two decomposition strategies compete and collaborate to solve a fully-separable problem. Each problem decomposition has features that may be beneficial at different stages of optimization. Therefore, competition and collaboration of such decomposition strategies may eliminate the need for finding an optimal decomposition. The experimental results in this paper suggest that com- petition and collaboration of suboptimal decomposition strategies of a fully-separable problem can generate better solutions than the standard cooperative coevolution with standalone decomposition strategies. We also show that a decomposition strategy that implements competition against itself can also improve the overall optimization performance.

    Item Type: Book Chapter
    Additional Information: DOI: 10.1007/978-3-319-26555-1_16 This paper was presented at the International Conference on Neural Information Processing, 9-12 November 2015, Istanbul, Turkey.
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Rohitash Chandra
    Date Deposited: 10 Mar 2016 16:24
    Last Modified: 10 Jun 2016 09:47
    URI: http://repository.usp.ac.fj/id/eprint/8427
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