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Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 13, Issue 9
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-03-27
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Richard Allmendinger, Carlos M. Fonseca, Serpil Sayin, Margaret M. Wiecek, and Michael Stiglmayr. Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361). In Dagstuhl Reports, Volume 13, Issue 9, pp. 1-68, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/DagRep.13.9.1
https://doi.org/10.4230/DagRep.13.9.1
Abstract
The Dagstuhl Seminar 23361 Multiobjective Optimization on a Budget carried on a series of seven previous Dagstuhl Seminars (04461, 06501, 09041, 12041, 15031, 18031, 20031) focused on Multiobjective Optimization. The original goal of this series has been to strengthen the links between the Evolutionary Multiobjective Optimization (EMO) and the Multiple Criteria Decision Making (MCDM) communities, two of the largest communities concerned with multiobjective optimization today. This seminar particularly focused on the case where the approaches from both communities may be challenged by limited resources.
This report documents the program and the outcomes of Dagstuhl Seminar 23361 "Multiobjective Optimization on a Budget". Three major types of resource limitations were highlighted during the seminar: methodological, technical and human related. The effect of these limitations on optimization and decision-making quality, as well as methods to quantify and mitigate this influence, were considered in different working groups.
Subject Classification
ACM Subject Classification
- Computing methodologies → Machine learning
- Computing methodologies → Neural networks
- Mathematics of computing → Evolutionary algorithms
- Theory of computation → Mathematical optimization
- Computing methodologies → Computational control theory
Keywords
- evolutionary algorithms
- expensive optimization
- few-shot learning
- machine learning
- optimization
- simulation
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