Abstract
In evolutionary optimization, the purpose of preselection is to identify some promising solutions in a set of candidate offspring solutions. The surrogate model is a popular method employed in preselection. A surrogate model is built to approximate the original objective function and to estimate the fitness values of the candidate solutions. Based on the estimated fitness values, the promising solutions can be identified. This paper aims to study and compare the surrogate model based preselection strategies in evolutionary algorithms. Systematic experiments are conducted to study the performance of four surrogate models. The experimental results suggest the surrogate model based preselection can significantly improve the performance of evolutionary algorithms.
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This work is supported by the National Natural Science Foundation of China under Grant No. 61731009, 61673180, and 61703382.
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Hao, H., Zhang, J., Zhou, A. (2018). A Comparison Study of Surrogate Model Based Preselection in Evolutionary Optimization. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_80
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