Abstract
This paper presents an efficient PARAllelization of Differential Evolution on GPU hardware written as an EASEA (EAsy Specification of Evolutionary Algorithms) template for easy reproducibility and re-use. We provide results of experiments to illustrate the relationship between population size and efficiency of the parallel version based on GPU related to the sequential version on the CPU. We also discuss how the population size influences the number of generations to obtain a certain level of result quality.
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References
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Arabas, J., Maitre, O., Collet, P. (2012). PARADE: A Massively Parallel Differential Evolution Template for EASEA. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_2
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DOI: https://doi.org/10.1007/978-3-642-29353-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29352-8
Online ISBN: 978-3-642-29353-5
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