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Erschienen in:
2010 | OriginalPaper | Buchkapitel
GAIS: A Gaussian Artificial Immune System for Continuous Optimization
verfasst von : Pablo A. D. Castro, Fernando J. Von Zuben
Erschienen in: Artificial Immune Systems
Verlag: Springer Berlin Heidelberg
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This paper proposes a Gaussian Artificial Immune System (GAIS) to deal effectively with building blocks (high-quality partial solutions coded in the solution vector) in continuous optimization problems. By replacing the mutation and cloning operators with a probabilistic model, more specifically a Gaussian network representing the joint distribution of promising solutions, GAIS takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions. Two versions of the algorithm were developed. In the first one, the estimation of the joint probability distribution is achieved by means of a single multivariate Gaussian distribution. In the second version, the estimation is carried out using a Gaussian mixture model. The algorithms were applied to eight benchmarks and the results compared with those produced by an immune-inspired algorithm and an estimation of distribution algorithm.