Attributes of dynamic combinatorial optimisation


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The field of evolutionary computation has traditionally focused on static optimisation problems but recently, many new approaches have been proposed that adapt traditional evolutionary algorithms to deal with the task of tracking high-quality solutions as the search space changes over time. Algorithms developed specifically for dynamic domains have been tested on a wide range of different problems, including well-specified benchmark generators. However, the lack of theoretical results, a general omission of references to actual real-world scenarios, as well as a substantial emphasis on the continuous domain may divert attention away from some highly relevant issues. Here we review the state of the field and analyse dynamics in the combinatorial domain, using the subset sum problem as an example. It is shown that some of the assumptions underlying the development of new algorithms do not necessarily hold in the case of discrete optimisation. Furthermore, it is argued that more attention should be paid to the underlying dynamics and the impact of the representation used. © 2008 Springer Berlin Heidelberg.
Original languageEnglish
Title of host publicationSimulated Evolution and Learning : 7th International Conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008, Proceedings
EditorsXiaodong LI, Michael KIRLEY, Mengjie ZHANG, David GREEN, Vic CIESIELSKI, Hussein ABBASS, Zbigniew MICHALEWICZ, Tim HENDTLASS, Kalyanmoy DEB, Kay Chen TAN, Jürgen BRANKE, Yuhui SHI
PublisherSpringer Berlin Heidelberg
Number of pages10
ISBN (Electronic)9783540896944
ISBN (Print)9783540896937
Publication statusPublished - 2008
Externally publishedYes
Event7th International Conference on Simulated Evolution and Learning, SEAL 2008 - Melbourne, Australia
Duration: 7 Dec 200810 Dec 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Simulated Evolution and Learning, SEAL 2008


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