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  • A population ecology inspired parent selection strategy for numerical constrained optimization problems

    Author(s)
    Yuchi, Ming
    Kim, Jong-Hwan
    Jo, Jun
    Griffith University Author(s)
    Jo, Jun
    Year published
    2007
    Metadata
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    Abstract
    A population ecology inspired parent selection strategy is proposed to improve the searching ability of evolutionary algorithms for numerical constrained optimization problems. This method is mainly used to help find an appropriate number of feasible parents for offspring generation. Based on the similar phenomenon in population ecology, the number of feasible parents has a sigmoid-type relationship with that of the feasible individuals. To implement the novel parent selection strategy, the population is divided into two groups according to the feasibility of the individuals: the feasible group and infeasible group. The ...
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    A population ecology inspired parent selection strategy is proposed to improve the searching ability of evolutionary algorithms for numerical constrained optimization problems. This method is mainly used to help find an appropriate number of feasible parents for offspring generation. Based on the similar phenomenon in population ecology, the number of feasible parents has a sigmoid-type relationship with that of the feasible individuals. To implement the novel parent selection strategy, the population is divided into two groups according to the feasibility of the individuals: the feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. The dynamic penalty method, annealing penalty method and stochastic ranking method are tested with the parent selection strategy on 13 benchmark problems. The results show that the proposed method is capable of improving the searching performance.
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    Journal Title
    Applied Mathematics and Computation (AMC)
    Volume
    190
    Issue
    1
    DOI
    https://doi.org/10.1016/j.amc.2007.01.027
    Subject
    Applied mathematics
    Numerical and computational mathematics
    Theory of computation
    Publication URI
    http://hdl.handle.net/10072/18503
    Collection
    • Journal articles

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