Abstract
Selecting the appropriate meta-features to represent the optimization problems was studied previously. However, the research on the extraction of meta-features for multi-objective problems is lacking. In this paper, a set of meta-features including a unique meta-feature based on Pareto front shape and the combination of meta-features are proposed for the multi-objective optimization problems (MOPs). 25 multi-objective benchmark functions and K-NN algorithm are adopted to realize the algorithm recommendation for MOPs. Experimental results show that the meta-features based on Pareto front can properly represent multi-objective problems and obtain better recommendation performance. The algorithm recommendation accuracy is improved once the combination of meta-features is considered.
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This work was partially supported by the National Natural Science Foundation of China (Grant No. 71971142 and 71701079).
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Chu, X. et al. (2021). Meta-feature Extraction for Multi-objective Optimization Problems. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_31
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DOI: https://doi.org/10.1007/978-981-16-5188-5_31
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