Research on RLGA-based Hardware Evolution Optimization Technology

Zhu, Ming; Yi, Sheng; Yang, Chunling; Feng, Rongwei

doi:10.1109/iciea48937.2020.9248179

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2022

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“…Combining Meta-heuristic with other methods is a feasible research direction. For example, Zhu et al [7] combined GA and reinforcement learning (RL) to optimize the FPGA circuit structure.…”

Section: Introductionmentioning

confidence: 99%

Deep Evolutionary Algorithm for Large-Scale Sequence Optimization

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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Aiming at the problem of slow convergence and poor effect of GA in solving large-scale sequence optimization problems, a deep evolutionary algorithm is proposed in this work. The algorithm uses the trained network to quickly find the initial solution of the problem and injects the initial solution into the GA population for further optimization. Finally, the optimal solution in the final population will be further optimized by the 2-OPT. The proposed algorithm is compared with other algorithms on the tsplib95 instances and industrial sorting sequence optimization dataset. Experimental results show that the proposed algorithm achieves the best optimization performance compared with other algorithms, especially for large-scale sequence optimization problems.

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Section: Introductionmentioning

confidence: 99%