Bipolar Mating Tendency: Harmony Between the Best and the Worst Individuals

Evolutionary algorithms (EAs) are successfully employed to solve complex optimization problems such as network design problems, path finding problems, scheduling problems and social and economic planning. By using intelligent strategies, EAs iteratively improve the initial solution to produce new solutions. Particularly in genetic algorithms (GAs), one of the most known EAs, selection mechanism, has crucial importance due to its effect on the diversity of a population and convergence ability to optimal solution. As most of the selection methods of GAs favor the good solutions for reproductions, average or poor solutions are overlooked. Recently, we proposed a new selection method, called Bipolar Mating Tendency (BMT), that is based on Standard Tournament (ST) selection. Unlike the state-of-the-art selection methods of GAs, BMT diversifies the mating process with the inclusion of less favorable solutions. In this paper, the performance of BMT was compared with the performance of ST and prevalent selection methods that are also based on ST: restricted tournament selection, unbiased tournament selection, fine-grained tournament selection and cooperative selection. For the comparison, twenty-one well-known benchmark optimization functions and sixteen IEEE CEC 2018 test functions were utilized. Also, nonparametric statistical tests were performed to demonstrate the significance of the results. In order to test the effectiveness of GA-BMT (GA that uses BMT as a selection method), three real-world engineering problems were used. In addition to showing predominant results comparing to the other methods in test functions, BMT has also provided successful results in the problems.