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Implementation of GA Processor with Multiple Operators, Based on Subpopulation Architecture  

Cho Min-Sok (인하대 전자재료공학과)
Chung Duck-Jin (인하대 정보통신공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.52, no.5, 2003 , pp. 295-304 More about this Journal
Abstract
In this paper, we proposed a hardware-oriented Genetic Algorithm Processor(GAP) based on subpopulation architecture for high-performance convergence and reducing computation time. The proposed architecture was applied to enhancing population diversity for correspondence to premature convergence. In addition, the crossover operator selection and linear ranking subpop selection were newly employed for efficient exploration. As stochastic search space selection through linear ranking and suitable genetic operator selection with respect to the convergence state of each subpopulation was used, the elapsed time of searching optimal solution was shortened. In the experiments, the computation speed was increased by over $10\%$ compared to survival-based GA and Modified-tournament GA. Especially, increased by over $20\%$ in the multi-modal function. The proposed Subpop GA processor was implemented on FPGA device APEX EP20K600EBC652-3 of AGENT 2000 design kit.
Keywords
GAP; subpopulation; steady-state model;
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