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International Conference on Artificial Immune Systems

ICARIS 2010: Artificial Immune Systems pp 171–184Cite as

GAIS: A Gaussian Artificial Immune System for Continuous Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6209))

Abstract

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.

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Author information

Authors and Affiliations

  1. Laboratory of Bioinformatics and Bioinpired Computing (LBiC), Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (Unicamp), 6101, 13083-852, Campinas-SP, Brazil

    Pablo A. D. Castro & Fernando J. Von Zuben

Authors
  1. Pablo A. D. Castro
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  2. Fernando J. Von Zuben
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Editor information

Editors and Affiliations

  1. Centre for Emergent Computing, Napier University, 10 Colinton Road, EH10 5DT, Edinburgh, UK

    Emma Hart  & Chris McEwan  & 

  2. Departments of Computer Science and Electronics, University of York, YO10 5DD, Heslington, York, UK

    Jon Timmis

  3. School of Mathematics, Statistics and Actuarial Science, University of Kent, Cornwallis Building, Canterbury, CT2 7NF, Kent, UK

    Andy Hone

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Castro, P.A.D., Von Zuben, F.J. (2010). GAIS: A Gaussian Artificial Immune System for Continuous Optimization. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-14547-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14546-9

  • Online ISBN: 978-3-642-14547-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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