Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/111349
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DC Field | Value | Language |
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dc.contributor.author | Mahbub, M. | - |
dc.contributor.author | Wagner, M. | - |
dc.contributor.author | Crema, L. | - |
dc.contributor.editor | Wagner, M. | - |
dc.contributor.editor | Li, X. | - |
dc.contributor.editor | Hendtlass, T. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Lecture Notes in Artificial Intelligence, 2017 / Wagner, M., Li, X., Hendtlass, T. (ed./s), vol.10142, pp.241-253 | - |
dc.identifier.isbn | 3319516906 | - |
dc.identifier.isbn | 9783319516905 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | http://hdl.handle.net/2440/111349 | - |
dc.description | Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10142) | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Md. Shahriar Mahbub, Markus Wagner and Luigi Crema | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science; 10142 | - |
dc.rights | © Springer International Publishing AG 2017 | - |
dc.source.uri | http://www.springer.com/gp/book/9783319516905 | - |
dc.title | Multi-objective optimisation with multiple preferred regions | - |
dc.type | Conference paper | - |
dc.contributor.conference | 3rd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017) (31 Jan 2017 - 2 Feb 2017 : Geelong, AUSTRALIA) | - |
dc.identifier.doi | 10.1007/978-3-319-51691-2_21 | - |
dc.publisher.place | Cham, Switzerland | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DE160100850 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Wagner, M. [0000-0002-3124-0061] | - |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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