Interleaving Generation Evolutionary Algorithm with Precedence Evaluation of Tentative Offspring
Hayato Noguchi, Akari Sonoda, Tomohiro Harada, Ruck Thawonmas · 2020
This paper proposes the method to improve the CPU utilization by using precedence evaluation of tentative offspring for the previous method. The previous research proposed the Interleaving Generation Evolutionary Algorithm (IGEA) that generates individuals which parents are evaluated before the evaluation of all individuals completed. IGEA can reduce the execution time for the optimization with EA. We compare the proposed method with the original IGEA and investigate the effectiveness of the proposed method. The experimental results show that the proposed method has higher CPU utilization than the original IGEA.