Multi-objective optimisation with multiple preferred regions
Date
2017
Authors
Mahbub, M.
Wagner, M.
Crema, L.
Editors
Wagner, M.
Li, X.
Hendtlass, T.
Li, X.
Hendtlass, T.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Artificial Intelligence, 2017 / Wagner, M., Li, X., Hendtlass, T. (ed./s), vol.10142, pp.241-253
Statement of Responsibility
Md. Shahriar Mahbub, Markus Wagner and Luigi Crema
Conference Name
3rd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017) (31 Jan 2017 - 2 Feb 2017 : Geelong, AUSTRALIA)
Abstract
The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former technique requires knowledge over the true set of trade-offs (and a notion of “closeness”) in order to perform well, it is not trivial to encode a non-standard preference for the latter. With this article, we contribute to the set of algorithms that consider preferences. In particular, we propose the easy-to-use concept of “preferred regions” that can be used by laypeople, we explain algorithmic modifications of NSGAII and AGE, and we validate their effectiveness on benchmark problems and on a real-world problem.
School/Discipline
Dissertation Note
Provenance
Description
Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10142)
Access Status
Rights
© Springer International Publishing AG 2017