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What Is a Learning Classifier System?

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Learning Classifier Systems (IWLCS 1999)

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Abstract

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

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References

  1. Manu Ahluwalia and Larry Bull. A Genetic Programming-based Classifier System. In 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 Banzhaf et al. [4], pages 11–18.

    Google Scholar 

  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 LeBaron 1999a.

    Google Scholar 

  3. Thomas Bäck, editor. Proceedings of the 7th International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann: San Francisco CA, 1997.

    Google Scholar 

  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.

    Google Scholar 

  5. Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 1 — Effects. In 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 Banzhaf et al. [4], pages 19–26.

    Google Scholar 

  6. Alwyn Barry. Aliasing in XCS and the Consecutive State Problem: 2 — Solutions. In 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 Banzhaf et al. [4], pages 27–34.

    Google Scholar 

  7. John Tyler Bonner. The Evolution of Complexity. Princeton University Press, Princeton, New Jersey, 1988.

    Google Scholar 

  8. Lashon B. Booker. Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan, 1982.

    Google Scholar 

  9. Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 125–142. (this volume).

    Google Scholar 

  10. Lashon B. Booker, David E. Goldberg, and John H. Holland. Classifier Systems and Genetic Algorithms. Artificial Intelligence, 40:235–282, 1989.

    Article  Google Scholar 

  11. Leo W. Buss. The Evolution of Individuality. Princeton University Press, Princeton, New Jersey, 1987.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  15. Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning, 19:209–240, 1995.

    Google Scholar 

  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.

    Article  Google Scholar 

  17. Marco Dorigo and Marco Colombetti. Robot Shaping: An Experiment in Behavior Engineering. MIT Press/Bradford Books, 1998.

    Google Scholar 

  18. E.B. Baum. Toward a model of intelligence as an economy of agents. Machine Learning, 35:155–185, 1999.

    Article  MATH  Google Scholar 

  19. J. Doyne Farmer, N. H. Packard, and A. S. Perelson. The Immune System, Adaptation & Learning. Physica D, 22:187–204, 1986.

    Article  MathSciNet  Google Scholar 

  20. Francine Federman and Susan Fife Dorchak. Information Theory and NEXT-PITCH: A Learning Classifier System. In Bäck [3], pages 442–449.

    Google Scholar 

  21. David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass., 1989.

    MATH  Google Scholar 

  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).

    Google Scholar 

  23. H. Hendriks-Jansen. Catching Ourselves in the Act. MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  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.

    Google Scholar 

  25. J. H. Holland, K. J. Holyoak, R. E. Nisbett, and P. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, 1986.

    Google Scholar 

  26. John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975. Republished by the MIT press, 1992.

    Google Scholar 

  27. John H. Holland. Adaptation. In R. Rosen and F. M. Snell, editors, Progress in theoretical biology. New York: Plenum, 1976.

    Google Scholar 

  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.

    Google Scholar 

  29. John H. Holland. Escaping brittleness. In Proceedings Second International Workshop on Machine Learning, pages 92–95, 1983.

    Google Scholar 

  30. John H. Holland. A Mathematical Framework for Studying Learning in Classifier Systems. Physica D, 22:307–317, 1986.

    MATH  MathSciNet  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  33. John H. Holland. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, Reading, MA, 1995.

    Google Scholar 

  34. John H. Holland and Arthur W. Burks. Adaptive Computing System Capable of Learning and Discovery. Patent 4697242 United States 29 Sept., 1987.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  37. John H. Holmes. Discovering Risk of Disease with a Learning Classifier System. In Bäck [3]. http://cceb.med.upenn.edu/holmes/icga97.ps.gz.

    Google Scholar 

  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.

    Article  Google Scholar 

  39. Kevin Kelly. Out of Control. Addison-Wesley, Reading, MA, 1994.

    Google Scholar 

  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.

    Google Scholar 

  41. Tim Kovacs. Strength or Accuracy? Fitness calculation in learning classifier systems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 143–160. (this volume).

    Google Scholar 

  42. Tim Kovacs and Pier Luca Lanzi. A Learning Classifier Systems Bibliography. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 323–350. (this volume).

    Google Scholar 

  43. John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, MA, 1994.

    MATH  Google Scholar 

  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.

    Google Scholar 

  45. Pier Luca Lanzi. A Study of the Generalization Capabilities of XCS. In Bäck [3], pages 418–425. http://ftp.elet.polimi.it/people/lanzi/icga97.ps.gz.

    Google Scholar 

  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.

    Google Scholar 

  48. Pier Luca Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation, 7(2):125–149, 1999.

    Article  Google Scholar 

  49. Pier Luca Lanzi and Rick L. Riolo. A Roadmap to the Last Decade of Learning Classifier System Research (from 1989 to 1999). In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 33–62. (this volume).

    Google Scholar 

  50. Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000.

    Google Scholar 

  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.

    Google Scholar 

  52. P.L. Lanzi and S. W. Wilson. Toward optimal classifier system performance in non-Markov environments. Evolutionary Computation, 2000. to appear.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  55. Richard E. Michod. Darwinian Dynamics: Evolutionary Transitions in Fitness and Individuality. Princeton University Press, Princeton, New Jersey, 1999.

    Google Scholar 

  56. Alan Newell and Herbert Simon. Human Problem Solving. Prentice Hall, Engel-wood Cliffs, NJ.

    Google Scholar 

  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).

    Google Scholar 

  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.

    Article  Google Scholar 

  59. Rick L. Riolo. Lookahead Planning and Latent Learning in a Classifier System. pages 316–326. A Bradford Book. MIT Press, 1990.

    Google Scholar 

  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.

    Google Scholar 

  61. Rick L. Riolo. Modeling Simple Human Category Learning with a Classifier System. pages 324–333. Morgan Kaufmann: San Francisco CA, July 1991.

    Google Scholar 

  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.

    Google Scholar 

  63. George G. Robertson and Rick L. Riolo. A Tale of Two Classifier Systems. Machine Learning, 3:139–159, 1988.

    Google Scholar 

  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.

    MathSciNet  Google Scholar 

  66. Shaun Saxon and Alwyn Barry. XCS and the Monk’s Problems. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 223–242. (this volume).

    Google Scholar 

  67. R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra. The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 285–302. (this volume).

    Google Scholar 

  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.

    Google Scholar 

  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/.

    Google Scholar 

  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 Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 175–194. (this volume).

    Google Scholar 

  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.

    Google Scholar 

  74. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning-An Introduction. MIT Press, 1998.

    Google Scholar 

  75. Kirk Twardowski. Implementation of a Genetic Algorithm based Associative Classifier System (ACS). In Proceedings International Conference on Tools for Artificial Intelligence, 1990.

    Google Scholar 

  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.

    Google Scholar 

  77. Nicolaas J. Vriend. The Difference Between Individual and Population Genetic Algorithms. In 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 Banzhaf et al. [4], pages 812–812.

    Google Scholar 

  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.

    Article  MATH  MathSciNet  Google Scholar 

  79. C.J.C.H. Watkins. Learning from delayed reward. PhD Thesis, Cambridge University, Cambridge, England, 1989.

    Google Scholar 

  80. Thomas H. Westerdale. An Approach to Credit Assignment in Classifier Systems. Complexity, 4(2), 1999.

    Google Scholar 

  81. Stewart W. Wilson. Adaptive “cortical” pattern recognition. pages 188–196. Lawrence Erlbaum Associates: Pittsburgh, PA, July 1985.

    Google Scholar 

  82. Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1–18, 1994. http://prediction-dynamics.com/.

    Article  Google Scholar 

  83. Stewart W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149–175, 1995. http://prediction-dynamics.com/.

    Article  Google Scholar 

  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 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 Koza et al. [44], pages 665–674. http://prediction-dynamics.com/.

    Google Scholar 

  86. Stewart W. Wilson. Get Real! XCS with Continuous-Valued Inputs. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 209–220. (this volume).

    Google Scholar 

  87. Stewart W. Wilson. State of XCS Classifier System Research. In Wolfgang Stolzmann, and Stewart W. Wilson, editors. Learning Classifier Systems: An Introduction to Contemporary Research, volume 1813 of LNAI. Springer-Verlag, Berlin, 2000 Lanzi et al. [50], pages 63–82. (this volume).

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Michigan, USA

    John H. Holland

  2. The MITRE Corporation, USA

    Lashon B. Booker

  3. Politecnico di Milano, Italy

    Marco Colombetti & Pier Luca Lanzi

  4. IRIDIA, Université Libre de Bruxelles, Belgium

    Marco Dorigo

  5. University of Illinois at Urbana-Champaign, USA

    David E. Goldberg & Stewart W. Wilson

  6. Santa Fe Institute, USA

    Stephanie Forrest

  7. Center for Study of Complex Systems, University of Michigan, USA

    Rick L. Riolo

  8. The Intelligent Computer Systems Centre, The University of The West of England, UK

    Robert E. Smith

  9. University of Wuerzburg, Germany

    Wolfgang Stolzmann

  10. Prediction Dynamics, USA

    Stewart W. Wilson

Authors
  1. John H. Holland
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  2. Lashon B. Booker
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  3. Marco Colombetti
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  4. Marco Dorigo
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  5. David E. Goldberg
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  6. Stephanie Forrest
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  7. Rick L. Riolo
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  8. Robert E. Smith
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  9. Pier Luca Lanzi
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  10. Wolfgang Stolzmann
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  11. Stewart W. Wilson
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica ed Informatzione, Politecnico di Milano, Piazza Leonardo da Vinci n. 32, 20133, Milano, Italy

    Pier Luca Lanzi

  2. Institut für Psychologie III, Universität Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Wolfgang Stolzmann

  3. Prediction Dynamics, Concord, MA, 01742, USA

    Stewart W. Wilson

  4. Department of General Engineering, University of Illinois at Urbana-Champaign, USA

    Stewart W. Wilson

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Holland, J.H. et al. (2000). What Is a Learning Classifier System?. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 1999. Lecture Notes in Computer Science(), vol 1813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45027-0_1

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