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- 01JGXFCH1CVQ5MKMSAVXETJZH7 classification C1.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 date "2025".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 language "eng".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 type conference.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 hasPart 01JGXFJEEHZ1FMJZG2Q9V5QN47.pdf.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 hasPart 01JGXFJMZNWA6E4VNRMYGEC4DD.pdf.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 subject "Technology and Engineering".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 doi "10.1007/978-3-031-74633-8_39".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 isbn "9783031746321".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 isbn "9783031746338".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 issn "1865-0929".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 issn "1865-0937".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 presentedAt urn:uuid:1012c6ee-b688-4e72-8d2a-1026ad5e41ce.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 abstract "Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives. However, the computation of the entire Pareto front can become prohibitively expensive, both in terms of computational resources and time, particularly when dealing with a large number of objectives. In practical applications, decision-makers (DMs) will select a single solution of the Pareto front that aligns with their preferences to be implemented; thus, traditional multi-objective algorithms invest a lot of budget sampling solutions that are not interesting for the DM. In this paper, we propose two novel algorithms that employ Gaussian Processes and advanced discretization methods to efficiently locate the most preferred region of the Pareto front in expensive-to-evaluate problems. Our approach involves interacting with the decision-maker to guide the optimization process towards their preferred trade-offs. Our experimental results demonstrate that our proposed algorithms are effective in finding non-dominated solutions that align with the decision-maker’s preferences while maintaining computational efficiency.".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 author 21a0a620-f314-11eb-8156-8eb675c744aa.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 author F945C52A-F0ED-11E1-A9DE-61C894A0A6B4.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 author FC259E64-F0ED-11E1-A9DE-61C894A0A6B4.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 author a608a0d5-0431-11eb-a74c-a85bbe704f8d.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 dateCreated "2025-01-06T09:33:25Z".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 dateModified "2025-01-23T08:06:04Z".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 editor urn:uuid:1a955982-4afa-4a2f-8167-6f539023ed58.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 editor urn:uuid:7cd36ada-1ee8-4aa9-9d3e-e89440a20fcf.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 name "Data-efficient interactive multi-objective optimization using ParEGO".
- 01JGXFCH1CVQ5MKMSAVXETJZH7 pagination urn:uuid:d863805d-5e11-4150-ba39-d7bf20e94e1b.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 publisher urn:uuid:44bb4f19-1149-46a3-93bd-150b196f5cbb.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 sameAs LU-01JGXFCH1CVQ5MKMSAVXETJZH7.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 sourceOrganization urn:uuid:1bd853fb-4c96-4392-b1b8-f9ab1581be42.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 sourceOrganization urn:uuid:edadb89a-588d-484b-814b-8ef9bdf0fb7b.
- 01JGXFCH1CVQ5MKMSAVXETJZH7 type C1.