Abstract
In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem’s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.
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Sallam, K.M., Elsayed, S.M., Sarker, R.A., Essam, D.L. (2017). Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_27
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DOI: https://doi.org/10.1007/978-3-319-49049-6_27
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