DagRep.10.1.52.pdf
- Filesize: 2.64 MB
- 78 pages
Document
Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)
Authors and all authors of the abstracts in this report
-
Part of:
Issue:
Dagstuhl Reports, Volume 10, Issue 1
Volume: Dagstuhl Reports, Volume 10
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2020-06-26
Cite AsGet BibTex
Carlos M. Fonseca, Kathrin Klamroth, Günter Rudolph, and Margaret M. Wiecek. Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031). In Dagstuhl Reports, Volume 10, Issue 1, pp. 52-129, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/DagRep.10.1.52
https://doi.org/10.4230/DagRep.10.1.52
Abstract
The Dagstuhl Seminar 20031 Scalability in Multiobjective Optimization carried on a series of six previous Dagstuhl Seminars (04461, 06501, 09041, 12041, 15031 and 18031) that were focused on Multiobjective Optimization. The continuing goal of this series is 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 report documents the program and the outcomes of Dagstuhl Seminar 20031 "Scalability in Multiobjective Optimization". The seminar focused on three main aspects of scalability in multiobjective optimization (MO) and their interplay, namely (1) MO with many objective functions, (2) MO with many decision makers, and (3) MO with many variables and large amounts of data.
Subject Classification
ACM Subject Classification
- Mathematics of computing → Mathematical optimization
- Applied computing → Operations research
- Computing methodologies → Artificial intelligence
Keywords
- multiple criteria decision making
- evolutionary multiobjective optimization
- scalability
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads