An experimental study of benchmarking functions for genetic algorithms

This paper presents a review and experimental results on the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the eect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.