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- W1597669675 abstract "This paper presents a new method that improves robustness of Real-Coded Evolutionary Algorithms (RCEAs), such as Real-Coded Genetic Algorithms and Evolution Strategies, for function optimization. It is reported that most crossover (or recombination) operators for RCEAs has sampling bias that prevents to find the optimum near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of the search space. Although several methods have been proposed to reduce this sampling bias, they could not cancel the whole bias. In this paper, we propose a new method, Toroidal Search Space Conversion (TSC), to remove this sampling bias. TSC converts bounded search space into toroidal one with no parameters. Experimental results show that a RCEA with TSC has higher performance to find the optimum near the boundary of search space and it has improved robustness concerning the relative position of the optimum." @default.
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- W1597669675 date "2002-07-09" @default.
- W1597669675 modified "2023-09-23" @default.
- W1597669675 title "Robust Evolutionary Algorithms with toroidal search space conversion for function optimization" @default.
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