Fitness landscape approximation by adaptive support vector regression with opposition-based learning

Yan Pei, Hideyuki Takagi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    8 Citations (Scopus)

    Abstract

    We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.

    Original languageEnglish
    Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
    Pages1329-1334
    Number of pages6
    DOIs
    Publication statusPublished - 2013
    Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
    Duration: Oct 13 2013Oct 16 2013

    Publication series

    NameProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

    Other

    Other2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
    Country/TerritoryUnited Kingdom
    CityManchester
    Period10/13/1310/16/13

    All Science Journal Classification (ASJC) codes

    • Human-Computer Interaction

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