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Classifier systems that compute action mappings

Published: 07 July 2007 Publication History

Abstract

The learning in a niche based learning classifier system depends both on the complexity of the problem space and on the number of available actions. In this paper, we introduce a version of XCS with computed actions, briefly XCSCA, that can be applied to problems involving a large number of actions. We report experimental results showing that XCSCA can evolve accurate and compact representations of binary functions which would be challenging for typical learning classifier system models.

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      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958
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      Published: 07 July 2007

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      Author Tags

      1. LCS
      2. XCS
      3. action mappings

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      GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      Cited By

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      • (2022)Deep Reinforcement Learning with a Classifier System – First StepsArchitecture of Computing Systems10.1007/978-3-031-21867-5_17(256-270)Online publication date: 14-Dec-2022
      • (2021)Enhancing learning classifier systems through convolutional autoencoder to classify underwater imagesSoft Computing10.1007/s00500-021-05738-wOnline publication date: 30-Mar-2021
      • (2021)An Evolutionary Approach to Combinatorial Gameplaying Using Extended Classifier SystemsApplications of Artificial Intelligence and Machine Learning10.1007/978-981-16-3067-5_54(723-738)Online publication date: 27-Jul-2021
      • (2018)Introducing learning classifier systemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207869(619-648)Online publication date: 6-Jul-2018
      • (2018)An Evolutionary Learning Approach to Play Othello Using XCS2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477644(1-8)Online publication date: Jul-2018
      • (2018)Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier SystemGenetic Programming Theory and Practice XV10.1007/978-3-319-90512-9_4(55-71)Online publication date: 6-Jul-2018
      • (2017)Extending xcs with cyclic graphs for scalability on complex boolean problemsEvolutionary Computation10.1162/EVCO_a_0016725:2(173-204)Online publication date: 1-Jun-2017
      • (2017)Introducing rule-based machine learningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067719(576-604)Online publication date: 15-Jul-2017
      • (2016)Introducing Rule-Based Machine LearningProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2926979(305-332)Online publication date: 20-Jul-2016
      • (2016)Adapting learning classifier systems to symbolic regression2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744061(2209-2216)Online publication date: Jul-2016
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