Balancing exploration and exploitation in genetic algorithm optimization: a novel selection operator

The effectiveness of genetic algorithms (GA) is dependent on the selection of operators utilized. A multitude of researchers have proposed a variety of operators with the aim of improving the performance of GA. The results demonstrate that achieving optimal outcomes necessitates a balance between exploration and exploitation. Prior to the implementation of crossover and mutation operators, the process of selecting parent individuals to produce offspring is of paramount importance in maintaining equilibrium. In this paper, we put forward a novel parent selection operator with the objective of improving the balance between exploration and exploitation. Moreover, proposed operator have been compared with existing operators in the literature in terms of convergence rate on a total of 30 distinct traveling salesman problems, 11 of which are symmetric and 19 of which are asymmetric. Finally, the statistical merit of the proposed operator is demonstrated through the use of a critical difference diagram (CD). The results obtained demonstrate that the proposed method is more effective than those presented in the existing literature.