A memetic cooperative co-evolution model for large scale continuous optimization

Yuan Sun*, Michael Kirley, Saman K. Halgamuge

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)

    Abstract

    Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorithm.

    Original languageEnglish
    Title of host publicationArtificial Life and Computational Intelligence - 3rd Australasian Conference, ACALCI 2017, Proceedings
    EditorsXiaodong Li, Markus Wagner, Tim Hendtlass
    PublisherSpringer Verlag
    Pages291-300
    Number of pages10
    ISBN (Print)9783319516905
    DOIs
    Publication statusPublished - 2017
    Event3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017 - Geelong, Australia
    Duration: 31 Jan 20172 Feb 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10142 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017
    Country/TerritoryAustralia
    CityGeelong
    Period31/01/172/02/17

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