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)
Year published
2007
Metadata
Show full item recordAbstract
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 ...
View more >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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View more >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.
View less >
Journal Title
Applied Mathematics and Computation (AMC)
Volume
190
Issue
1
Subject
Applied mathematics
Numerical and computational mathematics
Theory of computation