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Evolution of Affine Transformations and Iterated Function Systems Using Hierarchical Evolution Strategy

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Genetic Programming (EuroGP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2038))

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Abstract

Often optimization problems involve the discovery of many scalar coefficients. Although genetic programming (GP) has been applied to the optimization and discovery of functions with an arbitrary number of scalar coefficients, recent results indicate that a method for fine-tuning GP scalar terminals can assist the discovery of solutions. In this paper we demonstrate an approach where genetic programming and evolution strategies (ES) are seamlessly combined. We apply our GP/ES hybrid, which we name Hierarchical Evolution Strategy, to the problem of evolving affine transformations and iterated function systems (IFS). We compare the results of our approach with GP and notice an improvement in performance in terms of discovering bsetter solutions and speed.

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Author information

Authors and Affiliations

  1. Media School National Centre for Computer Animation, Bournemouth University, Talbot Campus Fern Barrow, Poole, Dorset, BH12 5BB, UK

    Anargyros Sarafopoulos

Authors
  1. Anargyros Sarafopoulos
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham

    Julian Miller

  2. Computer Science Institute, University of Lausanne, Lausanne

    Marco Tomassini

  3. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy

    Pier Luca Lanzi

  4. Computer Science and Information Systems, University of Limerick, Limerick

    Conor Ryan

  5. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  6. Department of Computer Science, University College London, London

    William B. Langdon

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© 2001 Springer-Verlag Berlin Heidelberg

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Sarafopoulos, A. (2001). Evolution of Affine Transformations and Iterated Function Systems Using Hierarchical Evolution Strategy. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_14

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  • DOI: https://doi.org/10.1007/3-540-45355-5_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41899-3

  • Online ISBN: 978-3-540-45355-0

  • eBook Packages: Springer Book Archive

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