Adaptive asynchrony in semi-asynchronous evolutionary algorithm based on performance prediction using search history
Tomohiro Harada · Proceedings of the Genetic and Evolutionary Computation Conference · 2018
This paper proposes an adaptation technique in an asynchronous evolutionary algorithm (EA) and verifies its effectiveness on multi-objective optimization problems. A parallel EA, which executes EA on a parallel computational environment, can be classified into two approaches, a synchronous EA and an asynchronous EA. A synchronous approach generates new population after all solutions are evaluated, while an asynchronous approach continuously generates a new solution immediately after one solution evaluation completes. Beside this, a semi-asynchronous EA was proposed that can vary the number of waited solution evaluations before generating new solutions, which parameter is called asynchrony. This paper explores a technique to adjust the asynchrony during the optimization process. For this purpose, the proposed method predicts the search performance of EA with different asynchronies from the simulation using search history, and chooses the best asynchrony depending on the predicted performance. To verify the effectiveness of the proposed method, this paper compares these approaches on multi-objective optimization problems. The experimental result reveals that the performance of the proposed method is equal to or better than that of the semi-asynchronous approach with appropriate asynchrony not depending on the variance of the evaluation time of solutions.