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Competitive Island-Based Cooperative Coevolution for Efficient Optimization of Large-Scale Fully-Separable Continuous Functions

  • Kavitesh K. BaliEmail author
  • Rohitash Chandra
  • Mohammad N. Omidvar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9491)

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 competition 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.

Keywords

Fitness Evaluation Decomposition Strategy Problem Decomposition Context Vector Optimal Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kavitesh K. Bali
    • 1
    • 2
    Email author
  • Rohitash Chandra
    • 1
    • 2
  • Mohammad N. Omidvar
    • 3
  1. 1.School of Computing Information and Mathematical SciencesUniversity of South PacificSuvaFiji
  2. 2.Artificial Intelligence and Cybernetics Research GroupSoftware FoundationNausoriFiji
  3. 3.School of Computer Science and ITRMIT University MelbourneMelbourneAustralia

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