Search Progress Dependent Parent Selection for Avoiding Evaluation Time Bias in Asynchronous Parallel Multi-Objective Evolutionary Algorithms
Tomohiro Harada · 2020
This paper proposes a new parent selection strategy for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms. Asynchronous parallel evolutionary algorithms have a problem to biased toward the search region with short evaluation time. The proposed method selects parent solutions that take into account the search progress of each solution. This paper conducts an experiment to investigate the effectiveness of the proposed method. The experiment uses NSGA-III, a multi-objective evolutionary algorithm, and compares the synchronous NSGA-III, the asynchronous NSGA-III, and the asynchronous NSGA-III with the proposed selection method. The experimental result reveals that the proposed method can reduce the effect of the evaluation time bias while reducing the computing time of the parallel NSGA-III.