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Nature-Inspired Computation in Engineering pp 63–90Cite as

Enhancing Cooperative Coevolution with Surrogate-Assisted Local Search

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 637))

Abstract

In recent years, an increasing effort has been devoted to the study of metaheuristics suitable for large-scale global optimization in the continuous domain. However, so far the optimization of high-dimensional functions that are also computationally expensive has attracted little research. To address such an issue, this chapter describes an approach in which fitness surrogates are exploited to enhance local search (LS) within the low-dimensional subcomponents of a cooperative coevolutionary (CC) optimizer. The chapter also includes a detailed discussion of the related literature and presents a preliminary experimentation based on typical benchmark functions. According to the results, the surrogate-assisted LS within subcomponents can significantly enhance the optimization ability of a CC algorithm.

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  1. DADU, University of Sassari, Alghero, Italy

    Giuseppe A. Trunfio

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    Xin-She Yang

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Trunfio, G.A. (2016). Enhancing Cooperative Coevolution with Surrogate-Assisted Local Search. In: Yang, XS. (eds) Nature-Inspired Computation in Engineering. Studies in Computational Intelligence, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-30235-5_4

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