PPSN 2020 Workshop on (Multimodal) Multi-Objective Optimization
Call for Participation in the Workshop on
(Multimodal) Multi-Objective Optimization (MMMOO):
Challenges, Characteristics, and Peculiarities
(to be held on September 5 or 6, 2020 at PPSN 2020 in Leiden, The Netherlands)
- Christian Grimme, University of Münster, Germany, email@example.com
- Pascal Kerschke, University of Münster, Germany, firstname.lastname@example.org
- Heike Trautmann, University of Münster, Germany, email@example.com
- Michael T.M. Emmerich, LIACS, Leiden University, The Netherlands, firstname.lastname@example.org
- Hao Wang, LIACS, Leiden University, The Netherlands, email@example.com
Scope of the Workshop
In practical applications, multi-objective (MO) optimization is usually treated secondarily due to its rather deterrent complexity/difficulty (compared to single-objective (SO) optimization problems). Therefore, practitioners in general scalarize their problems, e.g., by optimizing weighted sums of the underlying objectives. A major reason for such behavior is the much lesser tangibility of MO problems; it is extremely challenging to imagine interaction effects between >= 2 decision variables and >= 2 objectives simultaneously (let alone visualize them within a single plot).
Even researchers usually limit themselves to visualizing only the Pareto fronts of MO problems, i.e., the image of the set of MO global optima. As a result, our "knowledge" about MO problems is highly influenced by our understanding of SO problems. For instance, it is well-known that multimodality can be very challenging in SO optimization. Thus, for a long time, research simply inferred that such structures cause similar problems for optimizers in the MO setting. In consequence, such structures have regularly been considered for the design of MO benchmark problems. Yet, recent works have shown that multimodality might in fact even facilitate MO optimization.
Topics of Interest
In an attempt to reduce our knowledge deficit in this particular domain, the proposed workshop shall provide a platform for researchers to actively exchange ideas that improve our understanding of (multimodal) MO continuous optimization problems. We therefore welcome contributions related to the following non-exclusive list of topics:
- Characteristics of continuous and combinatorial MO optimization problems.
- High-level landscape characteristics (such as ridges, plateaus, etc.) as well as exploratory landscape features.
- Empirical and theoretical results on the transferability of structural properties from SO to MO problems.
- Multiobjectivization strategies for SO problems.
- Techniques for visualizing landscapes of (multimodal) MO problems.
- Algorithms and/or algorithm building blocks (e.g., operators, selection mechanisms) that are capable of handling or exploiting discovered challenges in problem structures (such as multimodality, ill-conditioned landscapes, etc.).
- Consequences for the design of benchmark problems and evaluation of existing test problem suites.
- Workshop (at PPSN 2020): September 5 or 6, 2020
Please help us raising awareness for this workshop and distribute the link to this website (http://www.erc.is/go/mmmoo2020).