Parameter space analysis of genetic algorithm using support vector regression
Hwi-Yeon Cho, Hye-Jin Kim, Yong-Hyuk Kim · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
Tuning various parameters coexisting in genetic algorithms (GAs) has a direct impact on the performance of GA. Because of this, finding a proper parameter value is challenging. In this study, we use support vector regression to show the appropriate parameter space of GA. Moreover, this was applied and analyzed to solving NK-landscape problems. As a result, we show the complexities and difficulties of GA parameter space through this paper.