Abstract
With over 700 entries, this is the most comprehensive bibliography of the machine learning systems introduced by John Holland.
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References
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Bagnall, A.J., Smith, G.D.: Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity. In: Banzhaf et al. [22], pp. 774 (One page poster paper)
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Barry, A.: The Emergence of High Level Structure in Classifier Systems - A Proposal. Irish Journal of Psychology 14(3), 480–498 (1993)
Barry, A.: Hierarchy Formulation Within Classifiers System - A Review. In: Goodman et al. [300], pp. 195–211
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Bonarini, A.: Evolutionary Learning of General Fuzzy Rules with Biased Evaluation Functions: Competition and Cooperation. In: Proc. 1st IEEE Conf. on Evolutionary Computation, pp. 51–56 (1994)
Bonarini, A.: Learning Behaviors Represented as Fuzzy Logic Controllers. In: Zimmermann, H.-J. (ed.) Second European Congress on Intelligent Techniques and Soft Computing - EUFIT 1994, Aachen, D, vol. 2, pp. 710–715. Verlag der Augustinus Buchhandlung (1994)
Bonarini, A.: Extending Q-learning to Fuzzy Classifier Systems. In: Gori, M., Soda, G. (eds.) AI*IA 1995. LNCS (LNAI), vol. 992, pp. 25–36. Springer, Heidelberg (1995)
Bonarini, A.: Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers. In: Herrera and Verdegay [336], pp. 447–466
Bonarini, A.: Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers. In: Herrera, F., Verdegay, J.L. (eds.) Genetic Algorithms and Soft Computing (Studies in Fuzziness), Berlin, D, vol. 8, pp. 447–466. Physica-Verlag, Heidelberg (1996)
Bonarini, A.: Evolutionary Learning of Fuzzy rules: competition and cooperation. In: Pedrycz, W. (ed.) Fuzzy Modelling: Paradigms and Practice, pp. 265–284. Kluwer Academic Press, Norwell (1996), ftp.elet.polimi.it/pub/Andrea.Bonarini/ELF/ELF-Pedrycz.ps.gz
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Bonarini, A.: Reinforcement Distribution to Fuzzy Classifiers. In: Proceedings of the IEEE World Congress on Computational Intelligence (WCCI) - Evolutionary Computation, pp. 51–56. IEEE Computer Press, Los Alamitos (1998)
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Bonarini, A., Dorigo, M., Maniezzo, V., Sorrenti, D.: AutonoMouse: An Experiment in Grounded Behaviors. In: Proceedings of GAA 1991 - Second Italian Workshop on Machine Learning, Bari, Italy (1991)
Bonelli, P., Parodi, A.: An Efficient Classifier System and its Experimental Comparison with two Representative learning methods on three medical domains. In: Booker and Belew [72], pp. 288–295
Bonelli, P., Parodi, A., Sen, S., Wilson, S.W.: NEWBOOLE: A Fast GBML System. In: International Conference on Machine Learning, San Mateo, California, pp. 153–159. Morgan Kaufmann, San Francisco (1990)
Booker, L.B.: Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan (1982)
Booker, L.B.: Improving the performance of genetic algorithms in classifier systems. In: Grefenstette [305], pp. 80–92
Booker, L.B.: Classifier Systems that Learn Internal World Models. Machine Learning 3, 161–192 (1988)
Booker, L.B.: Triggered rule discovery in classifier systems. In: Schaffer [563], pp. 265–274
Booker, L.B.: Instinct as an Inductive Bias for Learning Behavioral Sequences. In: Meyer and Wilson [476], pp. 230–237
Booker, L.B.: Representing Attribute-Based Concepts in a Classifier System. In: Rawlins [519], pp. 115–127
Booker, L.B.: Viewing Classifier Systems as an Integrated Architecture. In: Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS 1992) [2], October 6-8. NASA Johnson Space Center, Houston, Texas (1992)
Booker, L.B.: Do We Really Need to Estimate Rule Utilities in Classifier Systems? In: Wu [739], pp. 236–241
Booker, L.B.: Classifier systems, endogenous fitness, and delayed reward: A preliminary investigation. In: Proceedings of the International Workshop on Learning Classifier Systems (IWLCS 2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4] (2000) (extended abstract)
Booker, L.B.: Do We Really Need to Estimate Rule Utilities in Classifier Systems? In: Lanzi et al. [446], pp. 125–142
Booker, L.B.: Classifier systems, endogenous fitness, and delayed rewards: A preliminary investigation. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings ofthe Genetic an Evolutionary Computation Conference (GECCO 2001), San Francisco, California, USA, July 7-11, pp. 921–926 (2001)
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Booker, L.B., Riolo, R.L., Holland, J.H.: Learning and Representation in Classifier Systems. In: Honavar, V., Uhr, L. (eds.) Artificial Intelligence and Neural Networks, pp. 581–613. Academic Press, London (1994)
Browne, W.: The Development of an Industrial Learning Classifier System for Application to a Steel Hot Strip Mill. PhD thesis, University of Wales, Cardiff (1999)
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Browne, W., Holford, K., Moore, C., Bullock, J.: The implementation of a learning classifier system for parameter identification by signal processing of data from steel strip downcoilers. In: Augousti, A.T. (ed.) Software in Measurement. IEE Computer and Control Division (1996)
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Browne, W., Holford, K., Moore, C., Bullock, J.: A Practical Application of a Learning Classifier System in a Steel Hot Strip Mill. In: Smith et al. [597], pp. 611–614
Browne, W., Holford, K., Moore, C., Bullock, J.: 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)
Bull, L.: Artificial Symbiology: evolution in cooperative multi-agent environments. PhD thesis, University of the West of England (1995)
Bull, L.: On ZCS in Multi-agent Environments. In: Eiben, A.E., Baeck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 471–480. Springer, Heidelberg (1998)
Bull, L.: On Evolving Social Systems. Computational and Mathematical Organization Theory 5(3), 281–298 (1999)
Bull, L.: On using ZCS in a Simulated Continuous Double-Auction Market. In: Banzhaf et al. [22], pp. 83–90
Bull, L.: Simple markov models of the genetic algorithm in classifier systems: Accuracy-based fitness. In: Proceedings of the International Workshop on Learning Classifier Systems (IWLCS 2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4] (2000) (Extended abstract)
Bull, L.: Simple markov models of the genetic algorithm in classifier systems: Multi-step tasks. In: Proceedings of the International Workshop on Learning Classifier Systems (IWLCS 2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4] (2000) (Extended abstract)
Bull, L.: Lookahead and latent learning in ZCS. In: Langdon, W.B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., We-gener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, July 9-13, pp. 897–904. Morgan Kaufmann Publishers, San Francisco (2002)
Bull, L.: On accuracy-based fitness. Journal of Soft Computing 6(3-4), 154–161 (2002)
Bull, L., Fogarty, T.C.: Coevolving Communicating Classifier Systems for Tracking. In: Albrecht et al. [9], pp. 522–527
Bull, L., Fogarty, T.C.: Evolving Cooperative Communicating Classifier Systems. In: Sebald, A.V., Fogel, L.J. (eds.) Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 308–315 (1994)
Bull, L., Fogarty, T.C.: Parallel Evolution of Communicating Classifier Systems. In: Proceedings of the 1994 IEEE Conference on Evolutionary Computing, pp. 680–685. IEEE, Los Alamitos (1994)
Bull, L., Fogarty, T.C.: Evolutionary Computing in Cooperative Multi-Agent Systems. In: Sen, S. (ed.) Proceedings of the 1996 AAAI Symposium on Adaptation, Coevolution and Learning in Multi-Agent Systems, pp. 22–27. AAAI, Menlo Park (1996)
Bull, L., Fogarty, T.C.: Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 12–21. Springer, Heidelberg (1996)
Bull, L., Fogarty, T.C., Mikami, S., Thomas, J.G.: Adaptive Gait Acquisition using Multiagent Learning for Wall Climbing Robots. In: Automation and Robotics in Construction XII, pp. 80–86 (1995)
Bull, L., Fogarty, T.C., Snaith, M.: Evolutionin Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrupedal Robot. In: Eshelman [227], pp. 382–388
Bull, L., Holland, O.: Internal and External Representations: A Comparison in Evolving the Ability to Count. In: Proceedings of the First Annual Society for the Study of Artificial Intelligence and Simulated Behaviour Robotics Workshop, pp. 11–14 (1994)
Bull, L., Hurst, J.: Self-Adaptive Mutation in ZCS Controllers. In: Oates, M.J., Lanzi, P.L., Li, Y., Cagnoni, S., Corne, D.W., Fogarty, T.C., Poli, R., Smith, G.D. (eds.) EvoIASP 2000, EvoWorkshops 2000, EvoFlight 2000, EvoSCONDI 2000, EvoSTIM 2000, EvoTEL 2000, and EvoROB/EvoRobot 2000. LNCS, vol. 1803, pp. 339–346. Springer, Heidelberg (2000)
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Bull, L., Studley, M.: Consideration of multiple objectives in neural learning classifier systems. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, p. 549. Springer, Heidelberg (2002)
Bull, L., Wyatt, D., Parmee, I.: Towards the use of XCS in interactive evolutionary design. In: Langdon, W.B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) GECCO 2002: Proceedings ofthe Genetic and Evolutionary Computation Conference, p. 951. Morgan Kaufmann Publishers, San Francisco (2002)
Butz, M., Goldberg, D.E., Stolzmann, W.: New challenges for an ACS: Hard problems and possible solutions. Technical Report 99019, University of Illinois at Urbana-Champaign, Urbana, IL (October 1999)
Butz, M., Goldberg, D.E., Stolzmann, W.: The anticipatory classifier system and genetic generalization. Technical Report 2000032, Illinois Genetic Algorithms Laboratory (2000)
Butz, M., Stolzmann, W.: Action-Planning in Anticipatory Classifier System. In: Wu [739], pp. 242–249
Butz, M.V.: An Implementation of the XCS classifier system in C. Technical Report 99021, The Illinois Genetic Algorithms Laboratory (1999)
Butz, M.V.: XCSJava 1.0: An Implementation of the XCS classifier system in Java. Technical Report 2000027, Illinois Genetic Algorithms Laboratory (2000)
Butz, M.V.: An Algorithmic Description of ACS2. In: Lanzi et al. [448], pp. 211–229
Butz, M.V.: Biasing Exploration in an Anticipatory Learning Classifier System. In: Lanzi et al. [448], pp. 3–22
Butz, M.V., Goldberg, D.E.: Bounding the population size in XCS to ensure reproductive opportunities. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, D., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1844–1856. Springer, Heidelberg (2003)
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Butz, M.V., Goldberg, D.E., Stolzmann, W.: Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System - Part 2: Performance Analysis. In: Whitely et al. [699], pp. 42–49, Also Technical Report 2000006 of the Illinois Genetic Algorithms Laboratory
Butz, M.V., Goldberg, D.E., Stolzmann, W.: Investigating Generalization in the Anticipatory Classifier System. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917. Springer, Heidelberg (2000), Also technical report 2000014 of the Illinois Genetic Algorithms Laboratory
Butz, M.V., Goldberg, D.E., Stolzmann, W.: Probability-enhanced predictions in the anticipatory classifier system. In: Proceedings of the International Workshop on Learning Classifier Systems (IWLCS 2000), in the Joint Workshops of SAB 2000 and PPSN 2000 [4] (2000) (Extended abstract)
Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W.: How XCS Evolves Accurate Classifiers. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 927–934. Morgan Kaufmann, San Francisco (2001)
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Kovacs, T. (2003). The 2003 Learning Classifier Systems Bibliography. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 2002. Lecture Notes in Computer Science(), vol 2661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40029-5_11
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