A new fitness-based selection operator for genetic algorithms to maintain the equilibrium of selection pressure and population diversity
A genetic algorithm is one of the best optimization techniques for solving complex nature optimization problems. Different selection schemes have been proposed in the literature to address the major weaknesses of GA i.e., premature convergence and low computational efficiency. This article proposed a new selection operator that provides a better trade-off between selection pressure and population diversity while considering the relative importance of each individual. The average accuracy of the proposed operator has been measured by χ2 goodness of fit test. It has been performed on two different populations to show its consistency. Also, its performance has been evaluated on fourteen
benchmark problems while comparing it with competing selection operators. Results show the effective performance in terms of two statistics i.e., less average and standard deviation values. Further, the performance indexes and the GA convergence show that the proposed operator takes better care of selection pressure and population diversity.
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