Abstract
Different algorithms and strategies behave disparately for different types of problems. In practical problems, we cannot grasp the nature of the problem in advance, so it is difficult for the engineers to choose a proper method to solve the problem effectively. In this case, the strategy selection task based on fitness landscape analysis comes into being. This paper gives a preliminary study on mutation strategy selection on the basis of fitness landscape analysis for continuous real-parameter optimization based on differential evolution. Some fundamental features of the fitness landscape and the components of standard differential evolution algorithm are described in detail. A mutation strategy selection framework based on fitness landscape analysis is designed. Some different types of classifiers which are applied to the proposed framework are tested and compared.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (61922072, 61876169, 61673404, 61976237).
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Liang, J., Li, Y., Qu, B., Yu, K., Hu, Y. (2020). Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_23
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DOI: https://doi.org/10.1007/978-981-15-3425-6_23
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