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Multimodal dynamic optimization: From evolutionary algorithms to artificial immune systems

Nanas, Nikolaos and De Roeck, Anne (2007). Multimodal dynamic optimization: From evolutionary algorithms to artificial immune systems. In: NunesDeCastro, L.; VonZuben, F. J. and Knidel, H. eds. Artificial Immune Systems, Volume 4628. Berlin: Springer, pp. 13–24.

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Multimodal Dynamic Optimisation is a challenging problem, used in this paper as a framework for the qualitative comparison between Evolutionary Algorithms and Artificial Immune Systems. It is argued that while Evolutionary Algorithms have inherent diversity problems that do not allow them to successfully deal with multimodal dynamic optimisation, the biological immune system involves natural processes for maintaining and boosting diversity and thus serves well as a metaphor for tackling this problem. We review the basic evolutionary and immune-inspired approaches to multimodal dynamic optimisation, we identify correspondences and differences and point out essential computation elements.

Item Type: Book Chapter
ISBN: 3-540-73921-1, 978-3-540-73921-0
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
Other Departments > Vice-Chancellor's Office
Other Departments
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 15644
Depositing User: Colin Smith
Date Deposited: 20 Apr 2009 11:30
Last Modified: 15 Jan 2016 11:08
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