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Adaptation in Cooperative Coevolutionary Optimization

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Adaptation and Hybridization in Computational Intelligence

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 18))

Abstract

Cooperative Coevolution (CC) is a typical divide-and-conquer strategy to optimize large scale problems with evolutionary algorithms. In CC, the original search directions are grouped in a suitable number of subcomponents. Then, different subpopulations are assigned to the subcomponents and evolved using an optimization metaheuristic. To evaluate the fitness of individuals, the subpopulations cooperate by exchanging information. In this chapter we review some of the most relevant adaptive techniques proposed in the literature to enhance the effectiveness of CC. In addition, we present a preliminary version of a new adaptive CC algorithm that addresses the problem of distributing efficiently the computational effort between the different subcomponents.

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Author information

Authors and Affiliations

  1. DADU, University of Sassari, 07041, Alghero, SS, Italy

    Giuseppe A. Trunfio

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  1. Giuseppe A. Trunfio
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Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister Jr.

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Trunfio, G.A. (2015). Adaptation in Cooperative Coevolutionary Optimization. In: Fister, I., Fister Jr., I. (eds) Adaptation and Hybridization in Computational Intelligence. Adaptation, Learning, and Optimization, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-14400-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-14400-9_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14399-6

  • Online ISBN: 978-3-319-14400-9

  • eBook Packages: EngineeringEngineering (R0)

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