Co-adaptive learning classifier systems based on coevolution within dyna architecture

Chungyuan Huang*, Chuen-Tsai Sun

*Corresponding author for this work

Research output: Contribution to conferencePaper

Abstract

Learning classifier systems are a model for problem-independent and adaptive machine learning. As with evolutionary computations, parameter settings determine whether learning classifier systems can generate optimal solutions and whether it can do so efficiently. The authors propose a co-adaptive approach to controlling parameters for coevolution-based learning classifier systems. By taking advantage of the on-line incremental learning capability of such systems, solutions can be produced that completely cover a target problem. The system combines the advantages of both adaptive and self-adaptive parameter-control approaches. Using a coevolution model means that two learning classifier systems can operate in parallel to simultaneously solve target and parameter-setting problems. Furthermore, the approach needs very little time to become efficient in terms of latent learning, since it only requires small amounts of information on performance metrics during early run-time stages. Our experimental results show that the proposed system outperforms comparable models regardless of a problem's stationary/non-stationary status.

Original languageEnglish
Pages2179-2183
Number of pages5
StatePublished - 17 Sep 2004
EventWCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China
Duration: 15 Jun 200419 Jun 2004

Conference

ConferenceWCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
CountryChina
CityHangzhou
Period15/06/0419/06/04

Keywords

  • Coevolution
  • Genetic algorithms
  • Latent learning
  • Learning classifier systems
  • Parameter tuning

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  • Cite this

    Huang, C., & Sun, C-T. (2004). Co-adaptive learning classifier systems based on coevolution within dyna architecture. 2179-2183. Paper presented at WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings, Hangzhou, China.