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With over 700 entries, this is the most comprehensive bibliography of the machine learning systems introduced by John Holland.
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Emergent Computation. Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks. A special issue of Physica D. Stephanie Forrest, Ed.(1990)
Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS 1992), October 6-8. NASA Johnson Space Center, Houston, Texas (1992)
Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000). IEEE Press, Las Alamitos(2000)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 2000. LNCS (LNAI), vol. 1996. Springer, Heidelberg (2001); In the Joint Workshops of SAB 2000 and PPSN 2000 (2000)
Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001). IEEE Press, Los Alamitos (2001)
Aguilar, J., Cerrada, M.: Fuzzy classifier system and genetic programming on system identification problems. 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 and Evolutionary Computation Conference (GECCO 2001), San Francisco, California, USA, July 7-11, 2001, pp. 1245–1251. Morgan Kaufmann, San Francisco (2001)
Aguilar, J.L., Cerrada, M.: Reliability-Centered Maintenance Methodology-Based Fuzzy Classifier System Design for Fault Tolerance. In: Koza et al. [423], pp. 621 (One page paper)
Ahluwalia, M., Bull, L.: A Genetic Programming-based Classifier System. In: Banzhaf et al. [22], pp. 11–18
Albrecht, R.F., Steele, N.C., Reeves, C.R. (eds.): Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms. Springer, Heidelberg (1993)
Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.): Proceedings of the 1999 Congress on Evolutionary Computation CEC 1999, Washington, DC. IEEE Press, Los Alamitos (1999)
Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., Talyer, P.: Asset Pricing Under Endogenous Expectations in an Artificial Stock Market. Technical report, Santa Fe Institute (1996); This is the original version of LeBaron (1999a)
Bacardit, J., Garrell, J.M.: Evolution of adaptive discretization intervals for A rule-based genetic learning system. 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 of the Genetic and Evolutionary Computation Conference, p. 677. Morgan Kaufmann Publishers, San Francisco (2002)
Bacardit, J., Garrell, J.M.: Evolving multiple discretizations with adaptive intervals for a Pittsburgh rule-based learning classifier system. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, D., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Standish, R., Kendall, G., Wilson, S., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A.C., Dowsland, K., Jonoska, N., Miller, J. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 1818–1831. Springer, Heidelberg (2003)
Bäck, T. (ed.): Proceedings of the 7th International Conference on Genetic Algorithms (ICGA 1997). Morgan Kaufmann, San Francisco (1997)
Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Institute of Physics Publishing&Oxford University Press (1997), http://www.iop.org/Books/Catalogue/
Bäck, T., Hammel, U., Schwefel, H.-P.: Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation 1(1), 3–17 (1997)
Baghdadchi, J.: A Classifier Based Learning Model for Intelligent Agents. In: Whitely et al. [699], pp. 870 (One page poster paper)
Bagnall, A.J.: A Multi-Adaptive Agent Model of Generator Bidding in the UK Market in Electricity. In: Whitely et al. [699], pp. 605–612
Bagnall, A.J., Smith, G.D.: An Adaptive Agent Model for Generator Company Bidding in the UK Power Pool. In: Proceedings of Artificial Evolution (1999)
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)
Ball, N.R.: Towards the Development of Cognitive Maps in Classifier Systems. In: Albrecht et al. [9], pp. 712–718
Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.): Proceedings ofthe Genetic and Evolutionary Computation Conference (GECCO 1999). Morgan Kaufmann, San Francisco (1999)
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
Barry, A.: Aliasing in XCS and the Consecutive State Problem: 1 - Effects. In: Banzhaf et al. [22], pp. 19–26
Barry, A.: Aliasing in XCS and the Consecutive State Problem: 2 - Solutions. In: Banzhaf et al. [22], pp. 27–34
Barry, A.: Specifying Action Persistence within XCS. In: Whitely et al. [699], pp. 50–57
Barry, A.: XCS Performance and Population Structure within Multiple-Step Environments. PhD thesis, Queens University Belfast (2000)
Barry, A.: Limits in long path learning with XCS. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1832–1843. Springer, Heidelberg (2003)
Barry, A.M.: The stability of long action chains in xcs. Journal of Soft Computing 6(3-4), 183–199 (2002)
Barry, D.A.: A hierarchical xcs for long path environments. 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 of the Genetic and Evolutionary Computation Conference (GECCO 2001), San Francisco, California, USA, July 7-11, pp. 913–920. Morgan Kaufmann, San Francisco (2001)
Bauer, R.J.: Genetic Algorithms and Investment Strategies. Wiley Finance edn. John Wiley & Sons, Chichester (1994)
Baum, E.: Towards a model of intelligence as an economy of agents. Machine Learning 35(2), 155–185 (1999)
Baum, E., Durdanovic, I.: An Evolutionary Post Production 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)
Baum, E., Durdanovic, I.: An Artificial Economy of Post Production Systems. In: Lanzi et al. [448], pp. 3–20
Belew, R.K., Forrest, S.: Learning and Programming in Classifier Systems. Machine Learning 3, 193–223 (1988)
Belew, R.K., Gherrity, M.: Back Propagation for the Classifier System. In: Schaffer [563], pp. 275–281
Bernadó, E., Llorà, X., Garrell, J.M.: XCS and GALE: A Comparative Study of Two Learning Classifier Systems with Six Other Learning Algorithms on Classification Tasks. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 115–341. Springer, Heidelberg (2002); Short version publishe in Genetic and Evolutionary Compution Conference, GECCO 2001 (2001)
Bernadó, E., Llorà, X., Garrell, J.M.: Xcs and gale: A comparative study of two learning classifier systems on data mining. In: Lanzi et al. [448], pp. 115–132
Bersini, H., Varela, F.J.: Hints for Adaptive Problem Solving Gleaned From Immune Networks. In: Schwefel and Männer [572], pp. 343–354
Beunings, J., Bölkow, L., Heydemann, B., Kresling, B., Lieck-feld, C.-P., Mattheck, C., Nachtigall, W., Reichholf, J., Schmidt, B.J., Straa, V., Witt, R.: Bionik: Naturals Vorbild. WWF Dokumentationen. PRO FUTURA Verlag, Munchen (1993)
Biondi, J.: Robustness and evolution in an adaptive system application on classification task. In: Albrecht et al. [9], pp. 463–470
Bonarini, A.: ELF: Learning Incomplete Fuzzy Rule Sets for an Autonomous Robot. In: Zimmermann, H.-J. (ed.) First European Congress on Fuzzy and Intelligent Technologies - EUFIT 1993, Aachen, D, September 1993, vol. 1, pp. 69–75. Verlag der Augustinus Buchhandlung (1993)
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
Bonarini, A.: Anytime learning and adaptation of fuzzy logic behaviors. Adaptive Behavior 5(3-4), 281–315 (1997)
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)
Bonarini, A.: Comparing reinforcement learning algorithms applied to crisp and fuzzy learning classifier systems. In: Banzhaf et al. [22], pp. 52–59
Bonarini, A.: An Introduction to Learning Fuzzy Classifier Systems. In: Lanzi et al. [446], pp. 83–104
Bonarini, A., Basso, F.: Learning to compose fuzzy behaviors for autonomous agents. Int. Journal of Approximate Reasoning 17(4), 409–432 (1997)
Bonarini, A., Bonacina, C., Matteucci, M.: Fuzzy and crisp representation of real-valued input for learning classifier systems. In: Wu [739], pp. 228–235
Bonarini, A., Bonacina, C., Matteucci, M.: Fuzzy and Crisp Representations of Real-valued Input for Learning Classifier Systems. In: Lanzi et al. [446], pp. 107–124
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)
Booker, L.B.: A new approach to encoding actions in classifier systems (2001)
Booker, L.B., Belew, R.K. (eds.): Proceedings of the 4th International Conference on Genetic Algorithms (ICGA 1991). Morgan Kaufmann, San Francisco (1991)
Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier Systems and Genetic Algorithms. Artificial lntelligence 40, 235–282 (1989)
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)
Browne, W., Holford, K., Moore, C.: An Industry Based Development of the Learning Classifier System Technique. Submitted to 4th International Conference on Adaptive Computing in Design and Manufacturing, ACDM 2000 (2000)
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)
Browne, W., Holford, K., Moore, C., Bullock, J.: 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 1997, Swansea, Wales, September 2 (1997)
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)
Bull, L., Hurst, J., Tomlinson, A.: Mutation in Classifier System Controllers. In: et al. [228], pp. 460–467
Bull, L., O’Hara, T.: Accuracy-based neuro and neuro-fuzzy classifier systems. 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 of the Genetic and Evolutionary Computation Conference, July 9-13, pp. 905–911. Morgan Kaufmann Publishers, San Francisco (2002)
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)
Butz, M.V., Goldberg, D.E., Stolzmann, W.: Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System - Part 1: Theoretical Approach. In: Whitely et al. [699], pp. 34–41, Also Technical Report 2000005 of the Illinois Genetic Algorithms Laboratory
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)
Butz, M.V., Pelikan, M.: Analyzing the evolutionary pressures in xcs. 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 of the Genetic and Evolutionary Computation Conference (GECCO 2001), San Francisco, California, USA, July 7-11, pp. 935–942. Morgan Kaufmann, San Francisco (2001)
Butz, M.V., Sastry, K., Goldberg, D.E.: Tournament selection: Stable fitness pressure in XCS. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, D., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1857–1869. Springer, Heidelberg (2003)
Butz, M.V., Wilson, S.W.: An Algorithmic Description of XCS. Technical Report 2000017, Illinois Genetic Algorithms Laboratory (2000)
Butz, M.V., Wilson, S.W.: An Algorithmic Description of XCS. In: Lanzi et al. [447], pp. 253–272
Butz, M.V., Wilson, S.W.: An algorithmic description of xcs. Journal of Soft Computing 6(3-4), 144–153 (2002)
Camilli, A.: Classifier systems in massively parallel architectures. Master’s thesis, University of Pisa (1990) (in Italian)
Camilli, A., Meglio, R.D.: Sistemi a classificatori su architetture a paral-lelismo massiccio. Technical report, Univ. Delgi Studi di Pisa (1989)
Camilli, A., Meglio, R.D., Baiardi, F., Vanneschi, M., Montanari, D., Serra, R.: Classifier System Parallelization on MIMD Architectures. Technical Report 3/17, CNR (1990)
Cao, Y.J., Ireson, N., Bull, L., Miles, R.: Distributed Learning Control of Traffic Signals. 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. 117–126. Springer, Heidelberg (2000)
Cao, Y.J., Ireson, N.: Design of a Traffic Junction Controller using a Classifier System and Fuzzy Logic. In: Reusch, B. (ed.) Fuzzy Days 1999. LNCS, vol. 1625. Springer, Heidelberg (1999)
Carbonaro, A., Casadei, G., Palareti, A.: Genetic Algorithms and Classifier Systems in Simulating a Cooperative Behavior. In: Albrecht et al. [9], pp. 479–483
Carse, B.: Learning Anticipatory Behaviour Using a Delayed Action Classifier System. In: Fogarty [246], pp. 210–223
Carse, B., Fogarty, T.C.: A delayed-action classifier system for learning in temporal environments. In: Proceedings of the 1st IEEE Conference on Evolutionary Computation, vol. 2, pp. 670–673 (1994)
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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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