Forced Breeding Evolution for Numerical Optimization

Genetic Algorithm and Differential Evolution are widely utilized and emulated in the field of metaheuristic algorithms. Species achieve population evolution through crossover and mutation with a small number of individuals. However, this paper argues that the continuity of species should be based on the phenomenon of species reproduction. This phenomenon applies to various species, with typically more dominant individuals having greater mate selection priority, and vice versa. This approach not only preserves the essence of GA and DE but also imparts a more diverse search capability. Experimental results demonstrate that our proposed method not only incorporates some concepts from GA and DE but also ensures the preservation of solution structures, preventing easy entrapment in local optimum in high-dimensional problems.