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International Conference on Artificial Intelligence and Symbolic Mathematical Computing

AISMC 1992: Artificial Intelligence and Symbolic Mathematical Computing pp 294–305Cite as

Qualitative mathematical modelling of genetic algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 737))

Abstract

Genetic algorithms are adaptive search algorithms which generate and test a population of individuals where each individual corresponds to a solution. They have been successfully applied to a range of problems in both artificial intelligence research and industry. The selection of the optimal parameters for a genetic algorithm is often a problem. This is especially true if the genetic algorithm has a protracted run-time in which case the setting of the parameters by trial and error is often unrealistic. This paper proposes the use of probability distribution functions and random walks to model various operators used in genetic algorithms. In this way it is hoped that a qualitatively accurate model with a very short run-time can be produced.

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References

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Author information

Authors and Affiliations

  1. Artificial Intelligence Systems Research Group, School of Engineering and Computer Science, University of Durham, DH1 3LE, UK

    R. Garigliano & D. J. Nettleton

Authors
  1. R. Garigliano
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  2. D. J. Nettleton
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Editor information

Jacques Calmet John A. Campbell

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© 1993 Springer-Verlag Berlin Heidelberg

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Garigliano, R., Nettleton, D.J. (1993). Qualitative mathematical modelling of genetic algorithms. In: Calmet, J., Campbell, J.A. (eds) Artificial Intelligence and Symbolic Mathematical Computing. AISMC 1992. Lecture Notes in Computer Science, vol 737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57322-4_21

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  • DOI: https://doi.org/10.1007/3-540-57322-4_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57322-7

  • Online ISBN: 978-3-540-48063-1

  • eBook Packages: Springer Book Archive

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