Multi-species Generation Strategy-Based Vegetation Evolution

Jun Yu, Hideyuki Takagi

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

    3 Citations (Scopus)


    We propose a multi-species generation strategy to increase the diversity of seed individuals produced in the maturity operation of vegetation evolution (VEGE). Since the breeding patterns of real plants can be roughly divided into sexual reproduction and asexual one, the proposed strategy additionally introduces two different methods to simulate these two patterns. As our preliminary attempt of the simulation, a mature individual is splattered randomly in the neighbor local area of its parent individual with Gaussian distribution probability to simulate asexual reproduction, while a mature individual is generated by crossing randomly selected two different parent individuals to simulate sexual reproduction. Our proposed strategy consists of these two new reproduction methods and that of our original VEGE, and each mature individual in every generation randomly selects one of these three methods to generate seed individuals, which is analogous to different plant species using different mechanisms to breed. To evaluate the performance of our proposed strategy, we compare VEGE and (VEGE + the proposed generation strategy) on 28 benchmark functions of three different dimensions from the CEC 2013 test suit with 30 independent trial runs. The experimental results have confirmed that the proposed strategy can increase the diversity of seed individuals, accelerate the convergence of VEGE significantly, and become effective according to the increase of dimensions.

    Original languageEnglish
    Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728169293
    Publication statusPublished - Jul 2020
    Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
    Duration: Jul 19 2020Jul 24 2020

    Publication series

    Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings


    Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
    Country/TerritoryUnited Kingdom
    CityVirtual, Glasgow

    All Science Journal Classification (ASJC) codes

    • Control and Optimization
    • Decision Sciences (miscellaneous)
    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Hardware and Architecture


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