A very fast convergent evolutionary algorithm for satisfactory solutions

Abstract

As we know, genetic algorithm converges slowly. It is a natural contradiction when the situation appears with expensive objective function evaluating and satisfactory solutions being adequate. In this paper, a very fast convergent evolutionary algorithm (VFEA) is proposed with inner-outer hypercone crossover, problem dependent and search status involved mutation (PdSiMu). The offsprings produced by hypercone crossover are allowed to be outside the hypercone generated by rotating the parents around their bisectrix. PdSiMu utilizes the problem and evolving information quickly. VFEA is experimentally compared with five competitors based on ten classic 30 dimensional benchmarks. Experimental results indicate that VFEA can reach the accuracy of 10−4 − 10−1 for all the benchmarks within 1500 function evaluations. VFEA arrives significantly better performance than all its competitors with higher solution accuracy and stronger robustness.