An Experimental Study of Benchmarking Functions for Genetic Algorithms
Jason G. Digalakis, Konstantinos G. Margaritis · International Journal of Computer Mathematics · 2002
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 effect 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.