An artificial immune network for multimodal function optimization

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...

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Bibliographic Details
Main Authors: de Castro, L.N., Timmis, J.
Format: Conference Proceeding
Language:English
Subjects:
Artificial immune systems
Clustering algorithms
Collaborative work
Computer networks
Evolution (biology)
Genetic mutations
Immune system
Laboratories
Pathogens
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Summary: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 include: 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 locating and maintaining stable local optima solutions.
DOI:10.1109/CEC.2002.1007011