Adaptive differential evolution: A visual comparison

Differential evolution (DE) is a variant of the evolutionary algorithm and has good performance in solving continuous optimization problems. Two important parameters, F and CR, control the behaviors of the mutation and crossover operators in DE. Setting their values is critical, but the tuning process could be difficult and time-consuming. In the last decade, many adaptive DE have been proposed with various mechanisms to adjust the parameter values during the evolutionary process. Although these studies conducted numerical experiments and showed promising performance of the proposed algorithms, very few studies investigated and compared how the parameter values are adjusted by these algorithms. In this study, we compared six different types of adaptive DEs and observed their behaviors visually. Several interesting observations are made.