Abstract
We present a bibliography of all works we could find on Learning Classifier Systems (LCS) — the genetics-based machine learning systems introduced by John Holland. With over 400 entries, this is at present the largest bibliography on classifier systems in existence. We include a list of LCS resources on the world wide web.
Keywords
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References
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Will Browne. The Development of an Industrial Learning Classifier System for Application to a Steel Hot Strip Mill. PhD thesis, University of Wales, Cardiff, 1999.
Will Browne, Karen Holford, and Carolynne Moore. An Industry Based Development of the Learning Classifier System Technique. Submitted to: 4th International Conference on Adaptive Computing in Design and Manufacturing (ACDM 2000).
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. The implementation of a learning classifier system for parameter identification by signal processing of data from steel strip downcoilers. In A. T. Augousti, editor, Software in Measurement. IEE Computer and Control Division, 1996.
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. A Practical Application of a Learning Classifier System for Downcoiler Decision Support in a Steel Hot Strip Mill. Ironmaking and Steelmaking, 25(1):33–41, 1997. Engineering Doctorate Seminar’ 97. Swansea, Wales, Sept. 2nd, 1997.
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Will Browne, Karen Holford, Carolynne Moore, and John Bullock. An Industrial Learning Classifier System: The Importance of Pre-Processing Real Data and Choice of Alphabet. To appear in: Engineering Applications of Artificial Intelligence, 1999.
Bryan G. Spohn and Philip H. Crowley. Complexity of Strategies and the Evolution of Cooperation. In Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, Hitoshi Iba, and Rick Riolo, editors. Genetic Programming 1997: Proceedings of the Second Annual Conference. Morgan Kaufmann: San Francisco, CA, 1997 Koza et al. [263], pages 521–528.
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Larry Bull. Artificial Symbiogenesis. Artificial Life, 2(3):269–292, 1996.
Larry Bull. On ZCS in Multi-agent Environments. Lecture Notes in Computer Science, 1498:471–480, 1998.
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Larry Bull. On using ZCS in a Simulated Continuous Double-Auction Market. 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. [12], pages 83–90.
Larry Bull and Terence C. Fogarty. Coevolving Communicating Classifier Systems for Tracking. In Nigel C. Steele, and Colin R. Reeves, editors. Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms. Spring-Verlag, 1993 Albrecht et al. [5], pages 522–527.
Larry Bull and Terence C. Fogarty. Evolving Cooperative Communicating Classifier Systems. In A. V. Sebald and L. J. Fogel, editors, Proceedings of the Third Annual Conference on Evolutionary Programming, pages 308–315, 1994.
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Y. J. Cao, N. Ireson, Larry Bull, and R. Miles. Design of a Traffic Junction Controller using a Classifier System and Fuzzy Logic. In Proceedings of the Sixth International Conference on Computational Intelligence, Theory, and Applications. Springer-Verlag, 1999.
A. Carbonaro, G. Casadei, and A. Palareti. Genetic Algorithms and Classifier Systems in Simulating a Cooperative Behavior. In Nigel C. Steele, and Colin R. Reeves, editors. Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms. Spring-Verlag, 1993 Albrecht et al. [5], pages 479–483.
Brian Carse. Learning Anticipatory Behaviour Using a Delayed Action Classifier System. In Fogarty [160], pages 210–223.
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Brian Carse, Terence C. Fogarty, and A. Munro. Evolving fuzzy rule based controllers using genetic algorithms. International Journal for Fuzzy Sets and Systems, 80:273–293, 1996.
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Lawrence Davis. Mapping Classifier Systems into Neural Networks. In Proceedings of the Workshop on Neural Information Processing Systems 1, pages 49–56, 1988.
Lawrence Davis, editor. Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence. Pitman Publishing: London, 1989.
Lawrence Davis. Mapping Neural Networks into Classifier Systems. In Schaffer [354], pages 375–378.
Lawrence Davis. Covering and Memory in Classifier Systems. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Lawrence Davis and David Orvosh. The Mating Pool: A Testbed for Experiments in the Evolution of Symbol Systems. In Eshelman [148], pages 405–--
Lawrence Davis, Stewart W. Wilson, and David Orvosh. Temporary Memory for Examples can Speed Learning in a Simple Adaptive System. In H. L. Roitblat and S. W. Wilson, editors. From Animals to Animats 2. Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92). A Bradford Book. MIT Press, 1992 Roitblat and Wilson [342], pages 313–320.
Lawrence Davis and D. K. Young. Classifier Systems with Hamming Weights. In Proceedings of the Fifth International Conference on Machine Learning, pages 162–173. Morgan Kaufmann, 1988.
Bart de Boer. Classifier Systems: a useful approach to machine learning? Master’s thesis, Leiden University, 1994. ftp://ftp.wi.leidenuniv.nl/pub/CS/MScTheses/deboer.94.ps.gz.
Kenneth A. De Jong. Learning with Genetic Algorithms: An Overview. Machine Learning, 3:121–138, 1988.
Daniel Derrig and James Johannes. Deleting End-of-Sequence Classifiers. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA, July 1998. Stanford University Bookstore.
Daniel Derrig and James D. Johannes. Hierarchical Exemplar Based Credit Allocation for Genetic Classifier Systems. 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. [262], pages 622–628.
L. Desjarlais and Stephanie Forrest. Linked learning in classifier systems: A control architecture for mobile robots. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
P. Devine, R. Paton, and M. Amos. Adaptation of Evolutionary Agents in Computational Ecologies. In BCEC-97, Sweden, 1997.
Jean-Yves Donnart and Jean-Arcady Meyer. A hierarchical classifier system implementing a motivationally autonomous animat. In Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB94). A Bradford Book. MIT Press, 1994 Cliff et al. [86], pages 144–153.
Jean-Yves Donnart and Jean-Arcady Meyer. Hierarchical-map Building and Self-positioning with MonaLysa. Adaptive Behavior, 5(1):29–74, 1996.
Jean-Yves Donnart and Jean-Arcady Meyer. Spatial Exploration, Map Learning, and Self-Positioning with MonaLysa. In Maja J. Mataric, Jean-Arcady Meyer, Jordan Pollack, and Stewart W. Wilson, editors. From Animals to Animats 4. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB96). A Bradford Book. MIT Press, 1996 Maes et al. [284], pages 204–213.
Marco Dorigo. Message-Based Bucket Brigade: An Algorithm for the Apportionment of Credit Problem. In Y. Kodratoff, editor, Proceedings of European Working Session on Learning’ 91, Porto, Portugal, number 482 in Lecture notes in Artificial Intelligence, pages 235–244. Springer-Verlag, 1991.
Marco Dorigo. New perspectives about default hierarchies formation in learning classifier systems. In E. Ardizzone, E. Gaglio, and S. Sorbello, editors, Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence (AI*IA) on Trends in Artificial Intelligence, volume 549 of LNAI, pages 218–227, Palermo, Italy, October 1991. Springer Verlag.
Marco Dorigo. Using Transputers to Increase Speed and Flexibility of Genetic-based Machine Learning Systems. Microprocessing and Microprogramming, 34:147–152, 1991.
Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier System. Technical Report 92-011, Politecnico di Milano, 1992.
Marco Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italy, 1992. (In Italian).
Marco Dorigo. Genetic and Non-Genetic Operators in ALECSYS. Evolutionary Computation, 1(2):151–164, 1993. Also Tech. Report TR-92-075 International Computer Science Institute.
Marco Dorigo. Gli Algoritmi Genetici, i Sistemi a Classificatori e il Problema dell’Animat. Sistemi Intelligenti, 3(93):401–434, 1993. In Italian.
Marco Dorigo. Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning, 19:209–240, 1995.
Marco Dorigo. The Robot Shaping Approach to Behavior Engineering. Thése d’Agrégation de l’Enseignement Supérieur, Faculté des Sciences Appliquées, Université Libre de Bruxelles, pp.176, 1995.
Marco Dorigo and Hugues Bersini. A Comparison of Q-Learning and Classifier Systems. In Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB94). A Bradford Book. MIT Press, 1994 Cliff et al. [86], pages 248–255.
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.
Marco Dorigo and Marco Colombetti. The Role of the Trainer in Reinforcement Learning. In S. Mahadevan et al., editor, Proceedings of MLC-COLT’ 94 Workshop on Robot Learning, July 10th, New Brunswick, NJ, pages 37–45, 1994.
Marco Dorigo and Marco Colombetti. Précis of Robot Shaping: An Experiment in Behavior Engineering. Special Issue on Complete Agent Learning in Complex Environment, Adaptive Behavior, 5(3–4):391–405, 1997.
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Kovacs, T., Lanzi, P.L. (2000). A Learning Classifier Systems Bibliography. 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_17
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