Novelty in the generation of initial population for genetic algorithms

Abstract

This paper presents a method of generating the initial population of genetic algorithms (GAs) for continuous global optimization by using upper and lower bounds of variables instead of a pseudo-random sequence. In order to make population lead to a more reliable solution, the generated initial population is much more evenly distributed, which can avoid causing rapid clustering around an arbitrary local optimal. Another important point is that the simplicity of a population illustrates the more symmetry, self-similarity, repetitions, periodicity such that they guide the computational process to go ahead to desired aim. We design a GA based on this initial population for global numerical optimization with continuous variables. So, the obtained population is more evenly distributed and resulting GA process is more robust. We executed the proposed algorithm to solve 3 benchmark problems with 128 dimensions and very large number of local minimums. The results showed that the proposed algorithm can find optimal or near-to-optimal solutions.