A Guideline for Parameter Setting of an Evolutionary Algorithm using Optimal Latin Hypercube Design and Statistical Analysis

Abstract

It is well known that the performance of an evolutionary algorithm (EA) is highly dependent on the setting of EA parameters. There have been many studies on EA parameter setting, but no single method outperformed all other methods on all classes of problems as declared by the no free lunch theorem. In order to overcome the heavy computational burden usually required for parameter tuning, we propose an EA parameter tuning procedure applicable to any set of test problems. First, we employ an optimal Latin hypercube design (OLHD) in which parameters of an EA algorithm are set as factors. Next, using parameter settings sampled by OLHD, we run EAs to solve the test problems. Then, we statistically recommend parameter settings suitable for the test problems. Statistical evaluation of this application results suggests the best recombination method, and the recommended parameter ranges of population size, selection rate, and mutation rate are reduced by 93%, 80%, and 84%, respectively, compared to those usually used in parameter ranges. Finally, the recommended parameter setting is applied to the optimization problem of a high-speed spindle motor for a hard disk drive, and shows the effectiveness of the proposed procedure for a practical design optimization application.