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A global representation scheme for genetic algorithms

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Computational Intelligence Theory and Applications (Fuzzy Days 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1226))

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Abstract

Modelling the behaviour of genetic algorithms has concentrated on Markov chain analysis. However, Markov chains yield little insight into the dynamics of the underlying mechanics and processes. Thus, a framework and methodology for global modelling and visualisation of genetic algorithms is described, using tools from the field of Information Theory. Using Principal Component Analysis (PCA) based on the Karhunen-Loève transform, a generation (instance of a population) is transformed into a compact low dimensional eigenspace representation. A pattern vector (set of weights) is calculated for each population of strings, by projecting it into the eigenspace. A 3D manifold or global signature is derived from the set of computed pattern vectors.

Principal Components Analysis is applied to a GA parameterised by three encoding schemes — binary, E-code and Gray — and a test platform consisting of twelve functions. The resultant manifolds are described and correlated. The paper is concluded with a discussion of possible interpretations of the derived results, and potential extensions to the proposed methodology.

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Authors and Affiliations

  1. Dept. of Computer Science and Information Systems, University of Limerick, Ireland

    J. J. Collins & Malachy Eaton

Authors
  1. J. J. Collins
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  2. Malachy Eaton
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Editor information

Bernd Reusch

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© 1997 Springer-Verlag Berlin Heidelberg

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Collins, J.J., Eaton, M. (1997). A global representation scheme for genetic algorithms. In: Reusch, B. (eds) Computational Intelligence Theory and Applications. Fuzzy Days 1997. Lecture Notes in Computer Science, vol 1226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62868-1_92

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  • DOI: https://doi.org/10.1007/3-540-62868-1_92

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62868-2

  • Online ISBN: 978-3-540-69031-3

  • eBook Packages: Springer Book Archive

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