Investigating Innovized Progress Operators with Different ML Methods
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2096-9_8
Reference9 articles.
1. Bhasin, D., Swami, S., Sharma, S., Sah, S., Saxena, D.K., Deb, K.: Investigating innovized progress operators with different machine learning methods. In: Emmerich, M., Deutz, A., Wang, H., Kononova, A.V., Naujoks, B., Li, K., Miettinen, K., Yevseyeva, I. (eds.) Evolutionary Multi-Criterion Optimization, pp. 134–146. Springer Nature Switzerland, Cham (2023)
2. Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pp. 785–794. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785
3. Cover, T.: Estimation by the nearest neighbor rule. IEEE Trans. Inf. Theory 14(1), 50–55 (1968). https://doi.org/10.1109/TIT.1968.1054098
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5. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Trans. Evolut. Comput. 18(4), 577–601 (2014). https://doi.org/10.1109/TEVC.2013.2281535
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