Abstract
This article presents an analysis of the sensitiveness of evolutionary algorithms to the change of the random number generator when using a real-world problem —the fitting of a theoretical curve to an experimental data set— as test. On the one hand, the evolutionary algorithms selected: particle swarm algorithm, differential evolution and genetic algorithm are widely used in optimization problems. And, on the other hand, the random number generator used: Mersenne Twister and GCC rand(), are the most frequently linked to evolutionary algorithms, as well as they are considered as high-quality. As a consequence of this work, an assessment is stated about the sensitiveness of the evolutionary algorithms studied to the choice of the random number generator.
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Cárdenas-Montes, M., Vega-Rodríguez, M.A., Gómez-Iglesias, A. (2012). Real-World Problem for Checking the Sensitiveness of Evolutionary Algorithms to the Choice of the Random Number Generator. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_35
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DOI: https://doi.org/10.1007/978-3-642-28942-2_35
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