Abstract
Data-driven evolutionary algorithms (DDEAs) have attracted much attention in recent years, due to their effectiveness and advantages in solving expensive and complex optimization problems. In an offline data-driven evolutionary algorithm, no additional information is available to update surrogate models, so we need to ensure the quality of the surrogate models built before optimization. Based on the offline data-driven evolutionary algorithm using selective surrogate ensembles (DDEA-SE), we propose a trimmed bagging based DDEA-SE (TDDEA-SE) to construct a more accurate model pool. To this end, we use trimmed bagging to prune models with large errors calculated by out-of-bag samples, thus improving the accuracy of the selective surrogate ensembles and promoting the optimization. The experimental results on benchmark problems show that the proposed algorithm can strike a balance between diversity and accuracy of models, and its competitiveness on solving offline data-driven optimization problems.
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Acknowledgements
This work is supported by the National Key R and D Program of China for International S and T Cooperation Projects (2017YFE0103900), China Postdoctoral Science Foundation (2020M672359) and the Fundamental Research Funds for the Central Universities (HUST: 2019kfyXMBZ056).
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Shan, Y., Hou, Y., Wang, M., Xu, F. (2021). Trimmed Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles. In: Pan, L., Pang, S., Song, T., Gong, F. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2020. Communications in Computer and Information Science, vol 1363. Springer, Singapore. https://doi.org/10.1007/978-981-16-1354-8_10
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DOI: https://doi.org/10.1007/978-981-16-1354-8_10
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