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Evolution of Fitness Functions to Improve Heuristic Performance

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Learning and Intelligent Optimization (LION 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5313))

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Abstract

In this paper we introduce the variable fitness function which can be used to control the search direction of any search based optimisation heuristic where more than one objective can be defined, to improve heuristic performance. The method is applied to a multi-objective travelling salesman problem and the performance of heuristics enhanced with the variable fitness function is compared to the original heuristics, yielding significant improvements. The structure of the variable fitness functions is analysed and the search is visualised to better understand the process.

This work was funded by EPSRC and @Road Ltd, a Trimble Company under an EPSRC CASE studentship, which was made available through and facilitated by the Smith Institute for Industrial Mathematics and System Engineering.

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Author information

Authors and Affiliations

  1. MOSAIC Research Group, University of Bradford, Bradford, BD7 1DP, United Kingdom

    Stephen Remde, Peter Cowling, Keshav Dahal & Nic Colledge

Authors
  1. Stephen Remde
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  2. Peter Cowling
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  3. Keshav Dahal
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  4. Nic Colledge
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Editor information

Editors and Affiliations

  1. Dept. Computer Science, University of Bologna, Bologna, Italy

    Vittorio Maniezzo

  2. Università degli Studi di Trento, Trento, Italy

    Roberto Battiti

  3. Discrete Math and Complex Systems Department, Sandia National Laboratories, Albuquerque, New Mexico, USA

    Jean-Paul Watson

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Remde, S., Cowling, P., Dahal, K., Colledge, N. (2008). Evolution of Fitness Functions to Improve Heuristic Performance. In: Maniezzo, V., Battiti, R., Watson, JP. (eds) Learning and Intelligent Optimization. LION 2007. Lecture Notes in Computer Science, vol 5313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92695-5_16

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  • DOI: https://doi.org/10.1007/978-3-540-92695-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92694-8

  • Online ISBN: 978-3-540-92695-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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