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An Artificial Immune Network for Multimodal Optimisation

de Castro, Leandro N. and Timmis, Jon (2002) An Artificial Immune Network for Multimodal Optimisation. In: Proceedings of the 2002 Congress on Evolutionary Computation. IEEE, pp. 699-704. ISBN 0-7803-7282-4. (doi:10.1109/CEC.2002.1007011) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:13792)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/CEC.2002.1007011

Abstract

This paper presents the adaptation of an immune network model, originally proposed to perform information compression and data clustering, to solve multimodal function optimization problems. The algorithm is described, theoretically and empirically compared with similar approaches from the literature. The main features of the algorithm are automatic determination of the population size, combination of local with global search (exploitation plus exploration of the fitness landscape), defined convergence criterion, and capability of maintaining stable local optima solutions.

Item Type: Book section
DOI/Identification number: 10.1109/CEC.2002.1007011
Uncontrolled keywords: artificial immune systems, clonal selection, immune network, multimodal optimisation
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:00 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13792 (The current URI for this page, for reference purposes)
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