tutorial

Learning classifier systems

Author:

Martin V. ButzAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

Pages 2367 - 2388

https://doi.org/10.1145/1388969.1389059

Published: 12 July 2008 Publication History

Get Access

References

[1]

Bacardit, J. (2004). Pittsburgh Genetic-Based Machine Learning in the Dame Mining Era: Representations, Generalization, and Run Time. Computer Engineering Department, University of Ramon Llull, Barcelona, Spain.]]

Google Scholar

[2]

Barry, A. (2007). The Learning Classifier Systems Web. University of Bath. http://lcsweb.cs.bath.ac.uk/.]]

Google Scholar

[3]

Baum, E. B. (1999). Toward a model of intelligence as an economy of agents. Machine Learning, 35, 155--185.]]

Digital Library

Google Scholar

[4]

Bernadó-Mansilla, E., & Garrell-Guiu, J.M. (2003). Accuracy-based learning classifier systems: Models, analysis, and applications to classification tasks. Evolutionary Computation,11, 209--238.]]

Digital Library

Google Scholar

[5]

Bernadó, E.; Llorà, X., & Garrell, J.M. (2002). XCS and GALE: A comparative study of two learning classifier systems and six other learning algorithms on classification tasks. In Lanzi, P.L.; Stolzmann, W. & Wilson, S.W. (Eds.), Advances in Learning Classifier Systems (LNAI 2321) (pp. 115--132). Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[6]

Bonarini, A. (2000). An introduction to learning fuzzy classifier systems. In Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.), Learning classifier systems: From foundations to applications (LNAI 1813) (pp. 83--104). Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[7]

Booker, L. B. (1982). Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis. The University of Michigan, Ann Arbor, MI.]]

Digital Library

Google Scholar

[8]

Booker, L. B. (1985). Improving the performance of genetic algorithms in classifier systems. In Grefenstette, J. (Ed.), Proceedings of the 1st international conference on genetic algorithms and their application (ICGA-1985) (pp. 80--92). Pittsburg, PA: Lawrence Erlbaum Associates.]]

Digital Library

Google Scholar

[9]

Booker, L. B. (2000). Do we really need to estimate rule utility in classifier systems? In Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.), Learning classifier systems: From foundations to applications (LNAI 1813) (pp. 125--142). Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[10]

Booker, L. B. (2001). Classifier systems, endogenous fitness, and delayed rewards: A preliminary investigation. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), 921--926.]]

Google Scholar

[11]

Booker, L. B., Goldberg, D. E., & Holland, J. H. (1989). Classifier systems and genetic algorithms. Artificial Intelligence, 40, 235--282.]]

Digital Library

Google Scholar

[12]

Bull, L. (Ed.). Applications of learning classifier systems. Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[13]

Bull, L, & Hurst, J. (2002): ZCS redux. Evolutionary Computation, 10, 185--205.]]

Digital Library

Google Scholar

[14]

Bull, L. & Kovacs, T. (Eds.) (2005). Foundations of learning classifier systems. Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[15]

Bull, L., & O'Hara, T. (2002). Accuracy-based neuro and neuro-fuzzy classifier systems. Proceedings of the Fourth Genetic and Evolutionary Computation Conference (GECCO-2002), 905--911.]]

Digital Library

Google Scholar

[16]

Butz, M. V. (2002). Anticipatory learning classifier systems. Kluwer Academic Publishers, Boston, MA.]]

Google Scholar

[17]

Butz, M. V. (2006). Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design. Studies in Fuzziness and Soft Computing Series, Springer Verlag, Berlin Heidelberg, Germany.]]

Google Scholar

[18]

Butz, M. V., Goldberg, D. E. & Lanzi, P. L. (2005). Computational complexity of the XCS classifier system. In Bull, L. & Kovacs, T. (Eds.), Foundations of learning classifier systems (pp. 91--126). Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[19]

Butz, M. V., Goldberg, D. E., & Lanzi, P. L. (2005). Gradient descent methods in learning classifier systems: Improving XCS performance in multistep problems. IEEE Transactions on Evolutionary Computation, 9, 452--473.]]

Digital Library

Google Scholar

[20]

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2002). The anticipatory classifier system and genetic generalization. Natural Computing, 1, 427--467.]]

Digital Library

Google Scholar

[21]

Butz, M. V., Goldberg, D. E., & Tharakunnel, K. (2003). Analysis and improvement of fitness exploitation in XCS: Bounding models, tournament selection, and bilateral accuracy. Evolutionary Computation, 11, 239--277.]]

Digital Library

Google Scholar

[22]

Butz, M. V., & Hoffmann, J. (2002). Anticipations control behavior: Animal behavior in an anticipatory learning classifier system. Adaptive Behavior, 10, 75--96.]]

Digital Library

Google Scholar

[23]

Butz, M. V., Kovacs, T., Lanzi, P. L., & Wilson, S. W. (2004). Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation, 8, 28--46.]]

Digital Library

Google Scholar

[24]

Butz, M. V., Lanzi, P. L., & Wilson, S. W. (in press). Function Approximation with XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction. IEEE Transactions on Evolutionary Computation.]]

Digital Library

Google Scholar

[25]

Butz, M.V., & Stolzmann, W. (2002). An algorithmic description of ACS2. In Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.). Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321) (pp. 211--230). Berlin Heidelberg: Springer-Verlag.Butz, M. V., & Wilson, S. W. (2002). An Algorithmic Description of XCS. Soft Computing, 6, 144--153.]]

Digital Library

Google Scholar

[26]

Dixon, P. W., Corne, D. W., & Oates, M. J. (2003). A ruleset reduction algorithm for the XCS learning classifier system. In Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.), Learning classifier system: Fifth international workshop, IWLCS 2002 (LNAI 2661) (pp. 20--29). Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[27]

Gérard, P., Meyer, J., & Sigaud, O. (2005). Combining latent learning and dynamic programming in MACS. European Journal of Operational Research, 160, 614--637.]]

Crossref

Google Scholar

[28]

Gérard, P., & Sigaud, O. (2001). Adding a generalization mechanism to YACS. Proceedings of the Third Genetic and Evolutionary Computation Conference (GECCO-2001), 951--957.]]

Google Scholar

[29]

Goldberg, D. E. (1983). Computer-Aided Gas Pipeline Operation using Genetic Algorithms and Rule Learning. PhD thesis. The University of Michigan, Ann Arbor, MI.]]

Google Scholar

[30]

Goldberg, D. E. (1989). Genetic algorithms in search, optimization & machine learning. Addison-Wesley.]]

Digital Library

Google Scholar

[31]

Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.]]

Digital Library

Google Scholar

[32]

Holland, J.H. (1985). Properties of the bucket brigade algorithm. In Grefenstette, J. (Ed.), Proceedings of the 1st international conference on genetic algorithms and their application (ICGA-1985) (pp. 1--7). Pittsburg, PA: Lawrence Erlbaum Associates.]]

Digital Library

Google Scholar

[33]

Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction. Processes of inference, learning, and discovery. MIT Press.]]

Digital Library

Google Scholar

[34]

Holland, J. H., & Reitman, J. S. (1978). Cognitive systems based on adaptive algorithms. In Waterman, D. A., & Hayes-Roth, F. (Eds.), Pattern directed inference systems (pp. 313--329). Academic Press.]]

Google Scholar

[35]

Kovacs, T. (1996). Evolving Optimal Populations with XCS Classifier Systems. Master thesis. University of Birmingham, Birmingham, UK.]]

Google Scholar

[36]

Kovacs, T. (2007). A Learning Classifier System Bibliography. Department of Computer Science, University of Bristol, UK. http://www.cs.bris.ac.uk/~kovacs/lcs/search.html.]]

Google Scholar

[37]

Kovacs, T. (2004). Strength of accuracy: Credit assignment in learning classifier systems. Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[38]

Kovacs, T., Llorà, X., & Takadama, K. (in press). Advances at the frontier of LCS (LNCS 4399). Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[39]

Lanzi, P. L. (1999). Extending the representation of classifier conditions. Part II: From messy coding to S-expressions. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), 345--352.]]

Google Scholar

[40]

Lanzi, P. L. (2000). Adaptive agents with reinforcement learning and internal memory. From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior, 333--342.]]

Google Scholar

[41]

Lanzi, P. L., Loiacono, D., Wilson, S. W., & Goldberg, D.E. (2006). Classifier prediction based on tile coding. GECCO 2006: Genetic and Evolutionary Computation Conference, 1497--1504.]]

Digital Library

Google Scholar

[42]

Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.) (2000). Learning classifier systems: From foundations to applications (LNAI 1813). Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[43]

Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.) (2001). Advances in learning classifier systems: Third international workshop, IWLCS 2000 (LNAI 1996). Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[44]

Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.) (2002). Advances in learning classifier systems: 4th international workshop, IWLCS 2001 (LNAI 2321). Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

[45]

Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.) (2003). Learning classifier systems: 5th international workshop, IWLCS 2002 (LNAI 2661). Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[46]

Lanzi, P. L., & Wilson, S.W. (2000). Toward optimal classifier system performance in non-Markov environments. Evolutionary Computation, 8, 393--418]]

Digital Library

Google Scholar

[47]

Llorà, X., & Garrell, J. M. (2001). Knowledge independent data mining with fine-grained parallel evolutionary algorithms. Proceedings of the Third Genetic and Evolutionary Computation Conference (GECCO-2001), 461--468.]]

Google Scholar

[48]

Llorà, X., Goldberg, D. E., Traus, I., & Bernadóó, E. (2003). Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection. In Lanzi, P.L., Stolzmann, W. & Wilson, S.W. (Eds.). Learning classifier systems: 5th international workshop, IWLCS 2002 (LNAI 2661) (pp. 118--142). Berlin Heidelberg: Springer-Verlag.]]

Google Scholar

[49]

Riolo, R. L. (1991). Lookahead planning and latent learning in a classifier system. In Meyer, J. & Wilson, S.W. (Eds.). From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (pp. 316--326). MIT Press.]]

Digital Library

Google Scholar

[50]

Sigaud, O., & Wilson, S. W. (in press). Learning classifier systems: A survey. Journal of Soft Computing.]]

Digital Library

Google Scholar

[51]

Smith, S. F. (1980). A learning System Based on Genetic Adaptive Algorithms. PhD thesis, University of Pittsburgh, Pittsburgh, PA.]]

Digital Library

Google Scholar

[52]

Stolzmann, W. (1998). Anticipatory classifier systems, Genetic Programming 1998: Proceedings of the Third Annual Conference, 658--664.]]

Google Scholar

[53]

Sutton, R.S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. Proceedings of the Seventh International Conference on Machine Learning, 216--224.]]

Digital Library

Google Scholar

[54]

Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. MIT Press.]]

Digital Library

Google Scholar

[55]

Valenzuela-Rendón, M. (1991). The fuzzy classifier system: A classifier system for continuously varying variables. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA 1991), 346.353.]]

Google Scholar

[56]

Widrow, B., & Hoff, M. (1960). Adaptive switching circuits. Western Electronic Show and Convention, part 4 (pp. 96--104). New York: Convention Record.]]

Crossref

Google Scholar

[57]

Wilson, S. W. (1983). On the retino-cortical mapping. International Journal of Man-Machine Studies, 18, 361--389.]]

Crossref

Google Scholar

[58]

Wilson, S. W. (1985). Knowledge growth in an artificial animal. In Grefenstette, J. (Ed.), Proceedings of the 1st international conference on genetic algorithms and their application (ICGA-1985) (pp. 16--23). Pittsburg, PA: Lawrence Erlbaum Associates.]]

Digital Library

Google Scholar

[59]

Wilson, S. W. (1987). Classifier systems and the animat problem. Machine Learning, 2, 199--228.]]

Digital Library

Google Scholar

[60]

Wilson, S. W. (1994). ZCS: A zeroth level classifier system. Evolutionary Computation, 2, 1--18.]]

Digital Library

Google Scholar

[61]

Wilson, S. W. (1995). Classifier fitness based on accuracy. Evolutionary Computation, 3, 149--175.]]

Digital Library

Google Scholar

[62]

Wilson, S. W. (2002). Compact rulesets from XCSI. In Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.). Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321) (pp. 196--208). Berlin Heidelberg: Springer-Verlag.]]

Digital Library

Google Scholar

Cited By

View all

Brede MHansmeier TPlatzner MWagner M(2022)XCS on embedded systemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533977(2071-2079)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533977
Maravall DLope J(2009)ANLAGISProceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy10.1007/978-3-642-02264-7_11(97-106)Online publication date: 18-Jun-2009
https://dl.acm.org/doi/10.1007/978-3-642-02264-7_11
Maravall DDe Lope J(2008)Neuro granular networks with self-learning stochastic connectionsProceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I10.5555/1813488.1813625(1029-1036)Online publication date: 25-Nov-2008
https://dl.acm.org/doi/10.5555/1813488.1813625

Index Terms

Learning classifier systems
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
    2. Search methodologies
      1. Discrete space search
      2. Game tree search

Recommendations

Learning classifier system with average reward reinforcement learning

In the family of Learning Classifier Systems, the classifier system XCS is most widely used and investigated. However, the standard XCS has difficulties solving large multi-step problems, where long action chains are needed to get delayed rewards. Up to ...
Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference

Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic systems. Learning Classifier Systems (LCSs) are evolutionary machine ...
Learning classifier system equivalent with reinforcement learning with function approximation
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

We present an experimental comparison of the reinforcement process between Learning Classifier System (LCS) and Reinforcement Learning (RL) with function approximation (FA) method, regarding their generalization mechanisms. To validate our previous ...

Comments

Information & Contributors

Information

Published In

GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

July 2008

1182 pages

ISBN:9781605581316

DOI:10.1145/1388969

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
388
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)2

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Brede MHansmeier TPlatzner MWagner M(2022)XCS on embedded systemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533977(2071-2079)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533977
Maravall DLope J(2009)ANLAGISProceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy10.1007/978-3-642-02264-7_11(97-106)Online publication date: 18-Jun-2009
https://dl.acm.org/doi/10.1007/978-3-642-02264-7_11
Maravall DDe Lope J(2008)Neuro granular networks with self-learning stochastic connectionsProceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I10.5555/1813488.1813625(1029-1036)Online publication date: 25-Nov-2008
https://dl.acm.org/doi/10.5555/1813488.1813625

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

References

Cited By

Index Terms

Recommendations

Learning classifier system with average reward reinforcement learning

Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS

Learning classifier system equivalent with reinforcement learning with function approximation

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations