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.

DOI: https://doi.org/10.1007/978-3-540-73922-7

Abstract

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.

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About

• Item ORO ID
• 15644
• Item Type
• Book Section
• ISBN
• 3-540-73921-1, 978-3-540-73921-0
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Research Group
• Centre for Research in Computing (CRC)
• Depositing User
• Colin Smith