Pareto optimization with small data by learning across common objective spaces

In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with the dimensionality of the objective space. The challenge is exacerbated in expensive optimization domains where evaluation data is at a premium. To ove...

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Detalles Bibliográficos
Autores principales: Tan, Chin Sheng, Gupta, Abhishek, Ong, Yew-Soon, Pratama, Mahardhika, Tan, Puay Siew, Lam, Siew Kei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10185551/
https://www.ncbi.nlm.nih.gov/pubmed/37188695
http://dx.doi.org/10.1038/s41598-023-33414-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10185551/
https://www.ncbi.nlm.nih.gov/pubmed/37188695
http://dx.doi.org/10.1038/s41598-023-33414-6

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