Abstract
Differential evolution has shown tremendous success in solving different complex optimization problems. However, the performance is highly dependent on the selection of its parameters. Although many techniques have been introduced to adaptively (or self-adaptively) determine the parameters, the task is recognized as a tedious one. In this research, we investigate the use of evolutionary algorithms, such as covariance adaptation matrix evolution strategy, differential evolution and genetic algorithm, to self-adaptively determine the possible values of both the amplification factor and crossover rate. The performances of the algorithms are compared to each other, as well as to a standard differential algorithm, by solving a well-known set of benchmark problems. The experimental results show that such an approach can improve the performance of differential evolution, however further investigation is required to find the appropriate evolutionary algorithm for evolving parameters.
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Elsayed, S., Sarker, R. (2015). Evolving the Parameters of Differential Evolution Using Evolutionary Algorithms. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_40
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DOI: https://doi.org/10.1007/978-3-319-13359-1_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13358-4
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