| Title |
Implementation of GA Processor with Multiple Operators, Based on Subpopulation Architecture |
| Authors |
조민석(Cho Min-Sok) ; 정덕진(Chung Duck-Jin) |
| Keywords |
유전자 알고리즘 ; 진화형 하드웨어 GAP ; subpopulation ; steady-state model |
| 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. |