Adaptive Differential Evolution With Sorting Crossover Rate for Continuous Optimization Problems
提出一种改进的自适应差分进化算法JADE_sort，通过根据个体适应度排序分配交叉率，使优秀个体的组件更可能出现在后代中，实验表明该算法有效。
Differential evolution (DE) is one of the best evolutionary algorithms (EAs). The effort of improving its performance has received great research attentions, such as adaptive DE (JADE). Based on the analysis on the aspects that may improve the performance of JADE, we introduce a modified JADE version with sorting crossover rate (CR). In JADE, CR values are generated based on mean value and Gaussian distribution. In the proposed algorithm, a smaller CR value is assigned to individual with better fitness value. Therefore, the components of the individuals, which have better fitness values, can appear in the offspring with higher possibility. In addition, the better offspring generated from last iteration are supposed to have better schemes, hence these schemes are preserved in next offspring generation procedure. This modified version is called as JADE algorithm with sorting CR (JADE_sort). The experiments results with several excellent algorithms show the effectiveness of JADE_sort.