Preference-driven multi-objective GP search for regression models with new dominance principle and performance indicators-Reference-Cited by-同舟云学术

Preference-driven multi-objective GP search for regression models with new dominance principle and performance indicators

Published:2022-03-18 Issue:14 Volume:52 Page:15809-15823
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Liang Jiayu,Zheng Ludi,Wu Han,Xue Yu

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03228-6.pdf

Reference40 articles.

1. Abouhawwash M, Deb K (2021) Reference point based evolutionary multi-objective optimization algorithms with convergence properties using kktpm and asf metrics. J Heuristics 3:1–40

2. Bechikh S, Kessentini M, Said LB, Ghédira K (2015) Preference incorporation in evolutionary multiobjective optimization: A survey of the state-of-the-art. Advances in Computers 98:141–207

3. Said LB, Bechikh SK (2010) The r-dominance: A new dominance relation for interactive evolutionary multicriteria decision making. IEEE Transactions on Evolutionary Computation 14(5):801–818

4. Bi Y, Xue B, Zhang M (2021) Multi-objective genetic programming for feature learning in face recognition. Applied Soft Computing 103(4):107152

5. Cai X, Xiao Y, Li M (2020) A grid-based inverted generational distance for multi/many-objective optimization. IEEE Transactions on Evolutionary Computation PP(99):1–14

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Biochar-enhanced concrete mixes: Pioneering multi-objective optimization;Journal of Building Engineering;2024-07