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Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 pp 173–182Cite as

Multiobjective Genetic Programming of Agent Decision Strategies

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 303))

Abstract

This work describes a method to control a behaviour of intelligent data mining agent We developed an adaptive decision making system that utilizes genetic programming technique to evolve an agent’s decision strategy. The parameters of data mining task and current state of an agent are taken into account by tree structures evolved by genetic programming. Efficiency of decision strategies is compared from the perspectives of single and multi criteria optimization.

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Author information

Authors and Affiliations

  1. Department of Theoretical Computer Science, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic

    Martin Šlapák

  2. Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic

    Roman Neruda

Authors
  1. Martin Šlapák
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  2. Roman Neruda
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Editor information

Editors and Affiliations

  1. Department of Computer Science Faculty of Elec. Eng. and Comp. Sci., VŠB-TUO, Ostrava-Poruba, Czech Republic

    Pavel Kömer

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Washington, Alabama, USA

    Ajith Abraham

  3. Department of Computer Science Faculty of Elect. Eng. and Comp. Sci., VŠB-TUO, Ostrava-Poruba, Czech Republic

    Václav Snášel

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© 2014 Springer International Publishing Switzerland

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Šlapák, M., Neruda, R. (2014). Multiobjective Genetic Programming of Agent Decision Strategies. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-08156-4_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08155-7

  • Online ISBN: 978-3-319-08156-4

  • eBook Packages: EngineeringEngineering (R0)

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