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Genetic Programming for Subjective Fitness Function Identification

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Genetic Programming (EuroGP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3003))

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Abstract

This work addresses the common problem of modeling fitness functions for Interactive Evolutionary Systems. Such systems are necessarily slow because they need human interaction for the fundamental task of fitness allocation. The research presented here demonstrates that Genetic Programming can be used to learn subjective fitness functions from human subjects, using historical data from an Interactive Evolutionary system for producing pleasing drum patterns. The results indicate that GP is capable of performing symbolic regression even when the number of training cases is substantially less than the number of inputs.

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Author information

Authors and Affiliations

  1. Biocomputing and Developmental Systems Group, Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Dan Costelloe & Conor Ryan

Authors
  1. Dan Costelloe
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  2. Conor Ryan
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Editor information

Editors and Affiliations

  1. No Affiliation,  

    Maarten Keijzer

  2. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA

    Una-May O’Reilly

  3. University of Essex Department of Computing and Electronic Systems, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Simon Lucas

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Department of Computer Science, University of Idaho, ID 83844-1010, Moscow,  

    Terence Soule

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© 2004 Springer-Verlag Berlin Heidelberg

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Costelloe, D., Ryan, C. (2004). Genetic Programming for Subjective Fitness Function Identification. In: Keijzer, M., O’Reilly, UM., Lucas, S., Costa, E., Soule, T. (eds) Genetic Programming. EuroGP 2004. Lecture Notes in Computer Science, vol 3003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24650-3_24

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  • DOI: https://doi.org/10.1007/978-3-540-24650-3_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21346-8

  • Online ISBN: 978-3-540-24650-3

  • eBook Packages: Springer Book Archive

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