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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.
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Digalakis, J. G., & Margaritis, K. G. (2002). An Experimental Study of Benchmarking Functions for Genetic Algorithms. International Journal of Computer Mathematics, 79(4), 403–416. https://doi.org/10.1080/00207160210939
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