An adaptive differential evolution considering correlation of two algorithm parameters
Tetsuyuki Takahama, Setsuko Sakai · 2014
Differential Evolution (DE) is an evolutionary algorithm. DE has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multimodal functions. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on parameter control is JADE. In JADE, two parameter values are generated according to a probability density function which is learned by the parameter values in success cases, where the child is better than the parent. The values of two parameters are independently generated. In this study, we propose a new method where the values of two parameters are generated dependently using the correlation coefficient. In each generation of DE, the pairs of two parameter values in the success cases are stored and the correlation coefficient is obtained. The parameter F is generated according to Cauchy distribution. The parameter CR is generated according to normal distribution of which mean is modified using the generated value of F and the correlation coefficient. The effect of the proposed method is shown by solving thirteen benchmark problems.