A novel fitness allocation algorithm for maintaining a constant selective pressure during GA procedure

During the evolution procedure of GA, the fitness distribution of population is always unforeseeable since it varies with many factors such as the nonlinearity and multimodality of optimization problem, crossover and mutation algorithms, and the progress of evolution procedure. For the GAs using stochastic selection mechanisms, fitness distribution sometimes significantly impacts on the performance of selection process, so as to make it very ineffective in protecting superior individuals and preventing inferior individuals. In this study, a new fitness scaling method, named powered distance sums scaling (PDSS), is proposed to eliminate the influence of fitness distribution on stochastic selection. Unlike previous approaches, the new method uses the powered sums of fitness distance to substitute for both raw fitness values and ranking numbers for computing scaled fitness. It maintains a much more constant and consistent selective pressure in different conditions of optimization problem and GA algorithm design, and may help GA designers in balancing exploration and exploitation during evolution procedures. Empirical studies are employed to illustrate the new method that through using the new scaling technique the convergence speed of GA search becomes more controllable