Genetic algorithm with self-adaptive-inversion operator

Genetic algorithm (GA) has been known as a classical search algorithm for a large-space data. It works so well in the vast majority of applications. However, a problem concerning to selective pressure still remains. It is a challenge topic for many researchers. High selective pressure can speed up the convergence as well as cause to lost of diversity in a population. In this paper, we introduce a self-adaptive-inversion operator to give a survival chance for the worse chromosomes even high selective pressure is applied. Results of applying the proposed GA are presented and compared to the results of applying the conventional GA, on the optimization functions and real-world data.