Accelerating evolutionary computation using estimated convergence points

Jun Yu, Yan Pei, Hideyuki Takagi

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

    13 Citations (Scopus)

    Abstract

    We use the convergence points estimated by our proposed method as elite individuals for evolutionary computation and evaluate the acceleration effect and analyze the effect and computational cost. The worst individuals in population are replaced with the convergence points estimated from the moving vectors between parent individuals and their offspring; i.e. these convergence points are used as elite individuals. Differential evolution (DE) and 14 benchmark functions are used in our evaluation experiments. The experimental results show that use of the estimated convergence points as elite can accelerate DE search in spite of the calculation cost of the convergence points. We finally analyze the components of the proposed estimation method to improve cost-performance.

    Original languageEnglish
    Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1438-1444
    Number of pages7
    ISBN (Electronic)9781509006229
    DOIs
    Publication statusPublished - Nov 14 2016
    Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
    Duration: Jul 24 2016Jul 29 2016

    Publication series

    Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

    Other

    Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
    Country/TerritoryCanada
    CityVancouver
    Period7/24/167/29/16

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Modelling and Simulation
    • Computer Science Applications
    • Control and Optimization

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