Abstract
The Differential Evolution is a floating-point evolutionary algorithm that has demonstrated good performance on locating the global optima in a wide variety of problems and applications. It has mainly three tuning parameters and their choice is fundamental to ensure good quality solutions. Because of this, adaptive parameter control and self-adaptive parameter control had been object of research. We present a novel scheme for controlling two parameters of the Differential Evolution using fitness information of the population in each generation. The algorithm shows outstanding performance on a well known benchmark functions, improving the standard DE and comparable with similar algorithms.
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Reynoso-Meza, G., Sanchis, J., Blasco, X. (2009). An Adaptive Parameter Control for the Differential Evolution Algorithm. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_47
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DOI: https://doi.org/10.1007/978-3-642-02478-8_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
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