[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.5555/646371.689028guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

What Is a Learning Classifier System?

Published: 01 January 2000 Publication History

Abstract

We asked "What is a Learning Classifier System" to some of the best-known researchers in the field. These are their answers.

References

[1]
Manu Ahluwalia and Larry Bull. A Genetic Programming-based Classifier System. In Banzhaf et al., pages 11-18.
[2]
W. Brian Arthur, John H. Holland, Blake LeBaron, Richard Palmer, and Paul Talyer. Asset Pricing Under Endogenous Expectations in an Artificial Stock Market. Technical report, Santa Fe Institute, 1996. This is the original version of LeBaron1999a.
[3]
Thomas Bäck, editor. Proceedings of the 7th International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann: San Francisco CA, 1997.
[4]
Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA, 1999.
[5]
Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 1 - Effects. In Banzhaf et al., pages 19-26.
[6]
Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 2 - Solutions. In Banzhaf et al., pages 27-34.
[7]
John Tyler Bonner. The Evolution of Complexity. Princeton University Press, Princeton, New Jersey, 1988.
[8]
Lashon B. Booker. Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan, 1982.
[9]
Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Lanzi et al., pages 125-142. (this volume).
[10]
Lashon B. Booker, David E. Goldberg, and John H. Holland. Classifier Systems and Genetic Algorithms. Artificial Intelligence, 40:235-282, 1989.
[11]
Leo W. Buss. The Evolution of Individuality. Princeton University Press, Princeton, New Jersey, 1987.
[12]
H. J. Chiel and R. D. Beer. The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences, 20:553-557, 1997.
[13]
Marco Colombetti and Marco Dorigo. Evolutionary Computation in Behavior Engineering. In Evolutionary Computation: Theory and Applications, chapter 2, pages 37-80. World Scientific Publishing Co.: Singapore, 1999. Also Tech. Report. TR/IRIDIA/1996-1, IRIDIA, UniversitÉ Libre de Bruxelles.
[14]
Michael Sean Davis. A Computational Model of Affect Theory: Simulations of Reducer/ Augmenter and Learned Helplessness Phenomena. PhD thesis, Department of Psychology, University of Michigan, 2000.
[15]
Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning, 19:209-240, 1995.
[16]
Marco Dorigo and Marco Colombetti. Robot shaping: Developing autonomous agents through learning. Artificial Intelligence, 2:321-370, 1994. ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.05-AIJ94.ps.gz.
[17]
Marco Dorigo and Marco Colombetti. Robot Shaping: An Experiment in Behavior Engineering. MIT Press/Bradford Books, 1998.
[18]
E.B. Baum. Toward a model of intelligence as an economy of agents. Machine Learning, 35:155-185, 1999.
[19]
J. Doyne Farmer, N. H. Packard, and A. S. Perelson. The Immune System, Adaptation & Learning. Physica D, 22:187-204, 1986.
[20]
Francine Federman and Susan Fife Dorchak. Information Theory and NEXT-PITCH: A Learning Classifier System. In Bäck, pages 442-449.
[21]
David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.
[22]
David E. Goldberg. Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding. Machine Learning, 5:407-425, 1990. (Also TCGA tech report 88002, U. of Alabama).
[23]
H. Hendriks-Jansen. Catching Ourselves in the Act. MIT Press, Cambridge, MA, 1996.
[24]
S. Hofmeyr and S. Forrest. Immunity by design: An artificial immune system. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 1289-1296, San Francisco, CA, 1999. Morgan-Kaufmann.
[25]
J. H. Holland, K. J. Holyoak, R. E. Nisbett, and P. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, 1986.
[26]
John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. Republished by the MIT press, 1992.
[27]
John H. Holland. Adaptation. In R. Rosen and F. M. Snell, editors, Progress in theoretical biology. New York: Plenum, 1976.
[28]
John H. Holland. Adaptive algorithms for discovering and using general patterns in growing knowledge bases. International Journal of Policy Analysis and Information Systems, 4(3):245-268, 1980.
[29]
John H. Holland. Escaping brittleness. In Proceedings Second International Workshop on Machine Learning, pages 92-95, 1983.
[30]
John H. Holland. A Mathematical Framework for Studying Learning in Classifier Systems. Physica D, 22:307-317, 1986.
[31]
John H. Holland. Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Mitchell, Michalski, and Carbonell, editors, Machine learning, an artificial intelligence approach. Volume II, chapter 20, pages 593-623. Morgan Kaufmann, 1986.
[32]
John H. Holland. Concerning the Emergence of Tag-Mediated Lookahead in Classifier Systems. Special issue of Physica D (Vol. 42), 42:188-201, 1989.
[33]
John H. Holland. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, Reading, MA, 1995.
[34]
John H. Holland and Arthur W. Burks. Adaptive Computing System Capable of Learning and Discovery. Patent 4697242 United States 29 Sept., 1987.
[35]
John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and P. R. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, 1986.
[36]
John H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman and F. Hayes-Roth, editors, Pattern-directed inference systems. New York: Academic Press, 1978. Reprinted in: Evolutionary Computation. The Fossil Record. David B. Fogel (Ed.) IEEE Press, 1998. ISBN: 0-7803-3481-7.
[37]
John H. Holmes. Discovering Risk of Disease with a Learning Classifier System. In Bäck. http://cceb.med.upenn.edu/holmes/icga97.ps.gz.
[38]
Keith J. Holyoak, K. Koh, and Richard E. Nisbett. A Theory of Conditioning: Inductive Learning within Rule-Based Default Hierarchies. Psych. Review, 96:315- 340, 1990.
[39]
Kevin Kelly. Out of Control. Addison-Wesley, Reading, MA, 1994.
[40]
Tim Kovacs. Evolving Optimal Populations with XCS Classifier Systems. Master's thesis, School of Computer Science, University of Birmingham, Birmingham, U.K., 1996. Also tech. report CSR-96-17 and CSRP-96-17 ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-17.ps.gz.
[41]
Tim Kovacs. Strength or Accuracy? Fitness calculation in learning classifier systems. In Lanzi et al., pages 143-160. (this volume).
[42]
Tim Kovacs and Pier Luca Lanzi. A Learning Classifier Systems Bibliography. In Lanzi et al., pages 323-350. (this volume).
[43]
John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, MA, 1994.
[44]
John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1998: Proceedings of the Third Annual Conference. Morgan Kaufmann: San Francisco, CA, 1998.
[45]
Pier Luca Lanzi. A Study of the Generalization Capabilities of XCS. In Bäck, pages 418-425. http://ftp.elet.polimi.it/people/lanzi/icga97.ps.gz.
[46]
Pier Luca Lanzi. Adding Memory to XCS. In Proceedings of the IEEE Conference on Evolutionary Computation (ICEC98). IEEE Press, 1998. http://ftp.elet.polimi.it/people/lanzi/icec98.ps.gz.
[47]
Pier Luca Lanzi. Reinforcement Learning by Learning Classifier Systems. PhD thesis, Politecnico di Milano, 1998.
[48]
Pier Luca Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation, 7(2):125-149, 1999.
[49]
Pier Luca Lanzi and Rick L. Riolo. A Roadmap to the Last Decade of Learning Classi_er System Research (from 1989 to 1999). In Lanzi et al., pages 33-62. (this volume).
[50]
Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classi_er Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000.
[51]
Pier Luca Lanzi and Stewart W. Wilson. Optimal classifier system performance in non-Markov environments. Technical Report 99.36, Dipartimento di Elettronica e Informazione - Politecnico di Milano, 1999. Also IlliGAL tech. report 99022, University of Illinois.
[52]
P.L. Lanzi and S. W. Wilson. Toward optimal classifier system performance in non-Markov environments. Evolutionary Computation, 2000. to appear.
[53]
Blake Lebaron, W. Brian Arthur, and R. Palmer. The Time Series Properties of an Artificial Stock Market. Journal of Economic Dynamics and Control, 1999.
[54]
Ramon Marimon, Ellen McGrattan, and Thomas J. Sargent. Money as a Medium of Exchange in an Economy with Artificially Intelligent Agents. Journal of Economic Dynamics and Control, 14:329-373, 1990. Also Tech. Report 89-004, Santa Fe Institute, 1989.
[55]
Richard E. Michod. Darwinian Dynamics: Evolutionary Transitions in Fitness and Individuality. Princeton University Press, Princeton, New Jersey, 1999.
[56]
Alan Newell and Herbert Simon. Human Problem Solving. Prentice Hall, Engelwood Cliffs, NJ.
[57]
E. Oliveira, J.M. Fonseca, and N. Jennings. Learning to be competitive in the Market. 1999. Proceedings of the AAAI Workshop on Negotiation, Orlando (FL).
[58]
J. K. Percus, O. Percus, and A. S. Perelson. Predicting the size of the antibody combining region from consideration of efficient self/non-self discrimination. Proceedings of the National Academy of Science, 90:1691-1695, 1993.
[59]
Rick L. Riolo. Lookahead Planning and Latent Learning in a Classifier System. pages 316-326. A Bradford Book. MIT Press, 1990.
[60]
Rick L. Riolo. Lookahead planning and latent learning in a classifier system. Ann Arbor, MI, 1991. In the Proceedings of the Simulation of Adaptive Behavior Conference, MIT Press, 1991.
[61]
Rick L. Riolo. Modeling Simple Human Category Learning with a Classifier System. pages 324-333. Morgan Kaufmann: San Francisco CA, July 1991.
[62]
George G. Robertson. Parallel Implementation of Genetic Algorithms in a Classifier System. In John J. Grefenstette, editor, Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA87), pages 140-147, Cambridge, MA, July 1987. Lawrence Erlbaum Associates. Also Tech. Report TR-159 RL87-5 Thinking Machines Corporation.
[63]
George G. Robertson and Rick L. Riolo. A Tale of Two Classifier Systems. Machine Learning, 3:139-159, 1988.
[64]
S. A. Hofmeyr and S. Forrest. Architecture for an Artificial Immune System. Submitted to Evolutionary Computation. Available at http://www.cs.unm.edu/steveah/ecs.ps, 1999.
[65]
Samuel, A. L. Some Studies in Machine Learning Using the Game of Checkers. IBM Journ. R & D, 3:211-229, 1959. Reprinted in Feigenbaum, E., and Feldman, J. (eds.), Computer and Thoughts, pp. 71-105, New York: McGraw-Hill, 1963.
[66]
Shaun Saxon and Alwyn Barry. XCS and the Monk's Problems. In Lanzi et al., pages 223-242. (this volume).
[67]
R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra. The Fighter Aircraft LCS: A Case of Difierent LCS Goals and Techniques. In Lanzi et al., pages 285-302. (this volume).
[68]
Robert E. Smith, B. A. Dike, R. K. Mehra, B. Ravichandran, and A. El-Fallah. Classifier Systems in Combat: Two-sided Learning of Maneuvers for Advanced Fighter Aircraft. In Computer Methods in Applied Mechanics and Engineering. Elsevier, 1999.
[69]
Wolfgang Stolzmann. Learning Classifier Systems using the Cognitive Mechanism of Anticipatory Behavioral Control, detailed version. In Proceedings of the First European Workshop on Cognitive Modelling, pages 82-89. Berlin: TU, 1996. http://www.psychologie.uni-wuerzburg.de/stolzmann/.
[70]
Wolfgang Stolzmann. Two Applications of Anticipatory Classifier Systems (ACSs). In Proceedings of the 2nd European Conference on Cognitive Science, pages 68-73. Manchester, U.K., 1997. http://www.psychologie.uni-wuerzburg.de/stolzmann/.
[71]
Wolfgang Stolzmann. Anticipatory classifier systems. In Proceedings of the Third Annual Genetic Programming Conference, pages 658-664, San Francisco, CA, 1998. Morgan Kaufmann. http://www.psychologie.uni-wuerzburg.de/stolzmann/gp-98.ps.gz.
[72]
Wolfgang Stolzmann. An Introduction to Anticipatory Classifier Systems. In Lanzi et al., pages 175-194. (this volume).
[73]
Richard S. Sutton. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the Seventh International Conference on Machine Learning, pages 216-224, Austin, TX, 1990. Morgan Kaufmann.
[74]
Richard S. Sutton and Andrew G. Barto. Reinforcement Learning - An Introduction. MIT Press, 1998.
[75]
Kirk Twardowski. Implementation of a Genetic Algorithm based Associative Classifier System (ACS). In Proceedings International Conference on Tools for Artificial Intelligence, 1990.
[76]
Nicolaas J. Vriend. On Two Types of GA-Learning. In S.H. Chen, editor, Evolutionary Computation in Economics and Finance. Springer, 1999. in press.
[77]
Nicolaas J. Vriend. The Difference Between Individual and Population Genetic Algorithms. In Banzhaf et al., pages 812-812.
[78]
Nicolaas J. Vriend. An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses. Journal of Economic Dynamics and Control, 24:1-19, 2000.
[79]
C.J.C.H. Watkins. Learning from delayed reward. PhD Thesis, Cambridge University, Cambridge, England, 1989.
[80]
Thomas H. Westerdale. An Approach to Credit Assignment in Classifier Systems. Complexity, 4(2), 1999.
[81]
Stewart W. Wilson. Adaptive "cortical" pattern recognition. pages 188-196. Lawrence Erlbaum Associates: Pittsburgh, PA, July 1985.
[82]
Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1-18, 1994. http://prediction-dynamics.com/.
[83]
Stewart W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149-175, 1995. http://prediction-dynamics.com/.
[84]
Stewart W. Wilson. Generalization in XCS. Unpublished contribution to the ICML '96 Workshop on Evolutionary Computing and Machine Learning. http://prediction-dynamics.com/, 1996.
[85]
Stewart W. Wilson. Generalization in the XCS classifier system. In Koza et al., pages 665-674. http://prediction-dynamics.com/.
[86]
Stewart W. Wilson. Get Real! XCS with Continuous-Valued Inputs. In Lanzi et al., pages 209-220. (this volume).
[87]
Stewart W. Wilson. State of XCS Classifier System Research. In Lanzi et al., pages 63-82. (this volume).

Cited By

View all
  • (2018)Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithmNeural Computing and Applications10.1007/s00521-016-2683-z30:1(111-125)Online publication date: 1-Jul-2018
  • (2017)A computational analysis of general intelligence tests for evaluating cognitive developmentCognitive Systems Research10.1016/j.cogsys.2017.01.00643:C(100-118)Online publication date: 1-Jun-2017
  • (2016)Learning feature fusion strategies for various image types to detect salient objectsPattern Recognition10.1016/j.patcog.2016.05.02060:C(106-120)Online publication date: 1-Dec-2016
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings
Learning Classifier Systems, From Foundations to Applications
January 2000
346 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2000

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 14 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2018)Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithmNeural Computing and Applications10.1007/s00521-016-2683-z30:1(111-125)Online publication date: 1-Jul-2018
  • (2017)A computational analysis of general intelligence tests for evaluating cognitive developmentCognitive Systems Research10.1016/j.cogsys.2017.01.00643:C(100-118)Online publication date: 1-Jun-2017
  • (2016)Learning feature fusion strategies for various image types to detect salient objectsPattern Recognition10.1016/j.patcog.2016.05.02060:C(106-120)Online publication date: 1-Dec-2016
  • (2014)Salient object detection using learning classifiersystems that compute action mappingsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598371(525-532)Online publication date: 12-Jul-2014
  • (2013)Better manufacturing process organization using multi-agent self-organization and co-evolutionary classifier systemsApplied Soft Computing10.1016/j.asoc.2012.04.03313:3(1407-1418)Online publication date: 1-Mar-2013
  • (2010)Performance evaluation of evolutionary algorithms for road detectionProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830728(1331-1332)Online publication date: 7-Jul-2010
  • (2009)Learning classifier systemsJournal of Artificial Evolution and Applications10.5555/1644490.16444912009(1-25)Online publication date: 1-Jan-2009
  • (2009)On the appropriateness of evolutionary rule learning algorithms for malware detectionProceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers10.1145/1570256.1570370(2609-2616)Online publication date: 8-Jul-2009
  • (2009)Are evolutionary rule learning algorithms appropriate for malware detection?Proceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570233(1915-1916)Online publication date: 8-Jul-2009
  • (2009)An adaptive genetic-based signature learning system for intrusion detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2009.03.03636:10(12036-12043)Online publication date: 1-Dec-2009
  • Show More Cited By

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media