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With over 600 entries, this is by far the most comprehensive bibliography of the machine learning systems introduced by John Holland.
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References
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Alwyn Barry. Specifying Action Persistence within XCS. In Whitely et al. [586], pages 50–57.
Alwyn Barry. XCS Performance and Population Structure within Multiple-Step Environments. PhD thesis, Queens University Belfast, 2000.
Richard J. Bauer. Genetic Algorithms and Investment Strategies. Wiley Finance Editions. John Wiley & Sons, 1994.
Eric Baum and Igor Durdanovic. 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]. Extended abstract.
Richard K. Belew and Stephanie Forrest. Learning and Programming in Classifier Systems. Machine Learning, 3:193–223, 1988.
Richard K. Belew and Michael Gherrity. Back Propagation for the Classifier System. In Schaffer [463], pages 275–281.
Hugues Bersini and Francisco J. Varela. Hints for Adaptive Problem Solving Gleaned From Immune Networks. In Schwefel and Männer [470], pages 343–354.
Janine Beunings, Ludwig Bölkow, Bernd Heydemann, Biruta Kresling, Claus Peter Lieckfeld, Claus Mattheck, Werner Nachtigall, Josef Reichholf, Bertram J. Schmidt, Veronika Straaβ, and Reinhard Witt. Bionik: Natur als Vorbild. WWF Dokumentationen. PRO FUTURA Verlag, München, 1993.
J. Biondi. Robustness and evolution in an adaptive system application on classification task. In Albrecht et al. [7], pages 463–470.
Andrea Bonarini. ELF: Learning Incomplete Fuzzy Rule Sets for an Autonomous Robot. In Hans-Jürgen Zimmermann, editor, First European Congress on Fuzzy and Intelligent Technologies EUFIT’93, volume 1, pages 69–75, Aachen, D, September1993. Verlag der Augustinus Buchhandlung.
Andrea Bonarini. Evolutionary Learning of General Fuzzy Rules with Biased Evaluation Functions: Competition and Cooperation. Proc. 1st IEEE Conf. on Evolutionary Computation, pages 51–56, 1994.
Andrea Bonarini. Learning Behaviors Represented as Fuzzy Logic Controllers. In Hans-Jürgen Zimmermann, editor, Second European Congress on Intelligent Techniques and Soft Computing-EUFIT’94, volume 2, pages 710–715, Aachen, D, 1994. Verlag der Augustinus Buchhandlung.
Andrea Bonarini. Extending Q-learning to Fuzzy Classifier Systems. In Marco Gori and Giovanni Soda, editors, Proceedings of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence, volume 992 of LNAI, pages 25–36, Berlin, 1995. Springer.
Andrea Bonarini. Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers. In Herrera and Verdegay [274], pages 447–466.
Andrea Bonarini. Delayed Reinforcement, Fuzzy Q-Learning and Fuzzy Logic Controllers. In F. Herrera and J. L. Verdegay, editors, Genetic Algorithms and Soft Computing, (Studies in Fuzziness, 8), pages 447–466, Berlin, D, 1996. Physica-Verlag.
Andrea Bonarini. Evolutionary Learning of Fuzzy rules: competition and cooperation. In W. Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, pages 265–284. Norwell, MA: Kluwer Academic Press, 1996. ftp://ftp.elet.polimi.it/pub/Andrea.Bonarini/ELF/ELF-Pedrycz.ps.gz.
Andrea Bonarini. Anytime learning and adaptation of fuzzy logic behaviors. Adaptive Behavior, 5(3–4):281–315, 1997.
Andrea Bonarini. Reinforcement Distribution to Fuzzy Classifiers. In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI)-Evolutionary Computation, pages 51–56. IEEE Computer Press, 1998.
Andrea Bonarini. Comparing reinforcement learning algorithms applied to crisp and fuzzy learning classifier systems. In Banzhaf et al. [18], pages 52–59.
Andrea Bonarini. An Introduction to Learning Fuzzy Classifier Systems. In Lanzi et al. [364], pages 83–104.
Andrea Bonarini and Filippo Basso. Learning to compose fuzzy behaviors for autonomous agents. Int. Journal of Approximate Reasoning, 17(4):409–432, 1997.
Andrea Bonarini, Claudio Bonacina, and Matteo Matteucci. Fuzzy and crisp representation of real-valued input for learning classifier systems. In Wu [623], pages 228–235.
Andrea Bonarini, Claudio Bonacina, and Matteo Matteucci. Fuzzy and Crisp Representations of Real-valued Input for Learning Classifier Systems. In Lanzi et al. [364], pages 107–124.
Andrea Bonarini, Marco Dorigo, V. Maniezzo, and D. Sorrenti. AutonoMouse: An Experiment in Grounded Behaviors. In Proceedings of GAA91-Second Italian Workshop on Machine Learning, Bari, Italy, 1991.
Pierre Bonelli and Alexandre Parodi. An Efficient Classifier System and its Experimental Comparison with two Representative learning methods on three medical domains. In Booker and Belew [59], pages 288–295.
Pierre Bonelli, Alexandre Parodi, Sandip Sen, and Stewart W. Wilson. NEW-BOOLE: A Fast GBML System. In International Conference on Machine Learning, pages 153–159, San Mateo, California, 1990. Morgan Kaufmann.
Lashon B. Booker. Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan, 1982.
Lashon B. Booker. Improving the performance of genetic algorithms in classifier systems. In Grefenstette [250], pages 80–92.
Lashon B. Booker. Classifier Systems that Learn InternalWorld Models. Machine Learning, 3:161–192, 1988.
Lashon B. Booker. Triggered rule discovery in classifier systems. In Schaffer [463], pages 265–274.
Lashon B. Booker. Instinct as an Inductive Bias for Learning Behavioral Sequences. In Meyer and Wilson [385], pages 230–237.
Lashon B. Booker. Representing Attribute-Based Concepts in a Classifier System. In Rawlins [422], pages 115–127.
Lashon B. Booker. Viewing Classifier Systems as an Integrated Architecture. In Collected Abstracts for the First International Workshop on Learning Classifier System (IWLCS-92) [2]. October 6–8, NASA Johnson Space Center, Houston, Texas.
Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Wu [623], pages 236–241.
Lashon B. Booker. 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]. Extended abstract.
Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Lanzi et al. [364], pages 125–142.
Lashon B. Booker and Richard K. Belew, editors. Proceedings of the 4th International Conference on Genetic Algorithms (ICGA91). Morgan Kaufmann: San Francisco CA, July 1991.
Lashon B. Booker, David E. Goldberg, and John H. Holland. Classifier Systems and Genetic Algorithms. Artificial Intelligence, 40:235–282, 1989.
Lashon B. Booker, Rick L. Riolo, and John H. Holland. Learning and Representation in Classifier Systems. In Vassant Honavar and Leonard Uhr, editors, Artificial Intelligence and Neural Networks, pages 581–613. Academic Press, 1994.
H. Brown Cribbs III and Robert E. Smith. Classifier System Renaissance: New Analogies, New Directions. In Koza et al. [347], pages 547–552.
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.
Will Browne, Karen Holford, Carolynne Moore, and John Bullock. A Practical Application of a Learning Classifier System in a Steel Hot Strip Mill. In Smith et al. [491], pages 611–614.
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.
Larry Bull. Artificial Symbiology: evolution in cooperative multi-agent environments. PhD thesis, University of the West of England, 1995.
Larry Bull. On ZCS in Multi-agent Environments. Lecture Notes in Computer Science, 1498:471–480, 1998.
Larry Bull. On Evolving Social Systems. Computational and Mathematical Organization Theory, 5(3):281–298, 1999.
Larry Bull. On using ZCS in a Simulated Continuous Double-Auction Market. In Banzhaf et al. [18], pages 83–90.
Larry Bull. 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]. Extended abstract.
Larry Bull. 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]. Extended abstract.
Larry Bull and Terence C. Fogarty. Coevolving Communicating Classifier Systems for Tracking. In Albrecht et al. [7], 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.
Larry Bull and Terence C. Fogarty. Parallel Evolution of Communicating Classifier Systems. In Proceedings of the 1994 IEEE Conference on Evolutionary Computing, pages 680–685. IEEE, 1994.
Larry Bull and Terence C. Fogarty. Evolutionary Computing in Cooperative Multi-Agent Systems. In Sandip Sen, editor, Proceedings of the 1996 AAAI Symposium on Adaptation, Coevolution and Learning in Multi-Agent Systems, pages 22–27. AAAI, 1996.
Larry Bull and Terence C. Fogarty. Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis. In H-M. Voigt, W. Ebeling, I. Rechenberg, and H-P. Schwefel, editors, Parallel Problem Solving from Nature-PPSN IV, pages 12–21. Springer-Verlag, 1996.
Larry Bull, Terence C. Fogarty, S. Mikami, and J. G. Thomas. Adaptive Gait Acquisition using Multi-agent Learning for Wall Climbing Robots. In Automation and Robotics in Construction XII, pages 80–86, 1995.
Larry Bull, Terence C. Fogarty, and M. Snaith. Evolution in Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrupedal Robot. In Eshelman [186], pages 382–388.
Larry Bull and O. Holland. 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, pages 11–14, 1994.
Larry Bull and Jacob Hurst. Self-Adaptive Mutation in ZCS Controllers. In Proceedings of the EvoNet Workshops-EvoRob 2000, pages 339–346. Springer, 2000.
Larry Bull, Jacob Hurst, and Andy Tomlinson. Mutation in Classifier System Controllers. In et al. [187], pages 460–467.
Martin Butz, David E. Goldberg, and Wolfgang Stolzmann. New challenges for an ACS: Hard problems and possible solutions. Technical Report 99019, University of Illinois at Urbana-Champaign, Urbana, IL, October 1999.
Martin Butz, David E. Goldberg, and Wolfgang Stolzmann. The anticipatory classifier system and genetic generalization. Technical Report 2000032, Illinois Genetic Algorithms Laboratory, 2000.
Martin Butz and Wolfgang Stolzmann. Action-Planning in Anticipatory Classifier Systems. In Wu [623], pages 242–249.
Martin V. Butz. An Implementation of the XCS classifier system in C. Technical Report 99021, The Illinois Genetic Algorithms Laboratory, 1999.
Martin V. Butz. XCSJava 1.0: An Implementation of the XCS classifier system in Java. Technical Report 2000027, Illinois Genetic Algorithms Laboratory, 2000.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System-Part 1: Theoretical Approach. In Whitely et al. [586], pages 34–41. Also Technical Report 2000005 of the Illinois Genetic Algorithms Laboratory.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System-Part 2: Performance Analysis. In Whitely et al. [586], pages 42–49. Also Technical Report2000006 of the Illinois Genetic Algorithms Laboratory.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Investigating Generalization in the Anticipatory Classifier System. In Proceedings of Parallel Problem Solving from Nature (PPSN VI), 2000. Also tech. report 2000014 of the Illinois Genetic Algorithms Laboratory.
Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Probabilityenhanced 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]. Extended abstract.
Martin V. Butz and Stewart W. Wilson. An Algorithmic Description of XCS. Technical Report 2000017, Illinois Genetic Algorithms Laboratory, 2000.
Alessio Camilli. Classifier systems in massively parallel architectures. Master’s thesis, University of Pisa, 1990. (In Italian).
Alessio Camilli and Roberto Di Meglio. Sistemi a classificatori su architetture a parallelismo massiccio. Technical report, Univ. Delgi Studi di Pisa, 1989.
Alessio Camilli, Roberto Di Meglio, F. Baiardi, M. Vanneschi, D. Montanari, and R. Serra. Classifier System Parallelization on MIMD Architectures. Technical Report 3/17, CNR, 1990.
Y. J. Cao, N. Ireson, L. Bull, and R. Miles. Distributed Learning Control of Trafic Signals. In Proceedings of the EvoNet Workshops-EvoSCONDI 2000, pages 117–126. Springer, 2000.
Y. J. Cao, N. Ireson, Larry Bull, and R. Miles. Design of a Trafic 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 Albrecht et al. [7], pages 479–483.
Brian Carse. Learning Anticipatory Behaviour Using a Delayed Action Classifier System. In Fogarty [203], pages 210–223.
Brian Carse and Terence C. Fogarty. A delayed-action classifier system for learning in temporal environments. In Proceedings of the 1st IEEE Conference on Evolutionary Computation, volume 2, pages 670–673, 1994.
Brian Carse and Terence C. Fogarty. A Fuzzy Classifier System Using the Pittsburgh Approach. In Davidor and Schwefel [136], pages 260–269.
Brian Carse, Terence C. Fogarty, and A. Munro. Distributed Adaptive Routing Control in Communications Networks using a Temporal Fuzzy Classifier System. In Proceedings of the Fifth IEEE Conference on Fuzzy Systems, pages 2203–2207. IEEE, 1996.
Brian Carse, Terence C. Fogarty, and A. Munro. Evolutionary Learning of Controllers using Temporal Fuzzy Classifier Systems. In I. C. Parmee, editor, Proceedings of the Second Conference on Adaptive Computing in Engineering Design and Control, pages 174–180, 1996.
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.
Brian Carse, Terence C. Fogarty, and A. Munro. The Temporal Fuzzy Classifier System and its Application to Distributed Control in a Homogeneous Multi-Agent ecology. In Goodman et al. [246], pages 76–86.
Brian Carse, Terence C. Fogarty, and Alistair Munro. Evolving Temporal Fuzzy Rule-Bases for Distributed Routing Control in Telecommunication Networks. In Herrera and Verdegay [274], pages 467–488.
Brian Carse, Terence C. Fogarty, and Alistair Munro. Artificial evolution of fuzzy rule bases which represent time: A temporal fuzzy classifier system. International Journal of Intelligent Systems, 13(issue 10–11):905–927, 1998.
G. Casadei, A. Palareti, and G. Proli. Classifier System in Trafic Management. In Albrecht et al. [7], pages 620–627.
Keith Chalk and George D. Smith. Multi-Agent Classifier Systems and the Iterated Prisoner’s Dilemma. In Smith et al. [491], pages 615–618.
Keith W. Chalk and George D. Smith. The Co-evolution of Classifier Systems in a Competitive Environment. Poster presented at AISB94. Authors were from the University of East Anglia, U.K.
Pawel Cichosz. Reinforcement learning algorithms based on the methods of temporal differences. Master’s thesis, Institute of Computer Science, Warsaw University of Technology, 1994.
Pawel Cichosz. Reinforcement Learning by Truncating Temporal Differences. PhD thesis, Department of Electronics and Information Technology,Warsaw University of Technology, 1997.
Pawel Cichosz and Jan J. Mulawka. GBQL: A novel genetics-based reinforcement learning architecture. In Proceedings of the Third European Congress on Intelligent Techniques and Soft Computing (EUFIT’95), 1995.
Pawel Cichosz and Jan J. Mulawka. Faster temporal credit assignment in learning classifier systems. In Proceedings of the First Polish Conference on Evolutionary Algorithms (KAE-96), 1996.
Dave Cliff and Seth G. Bullock. Adding `Foveal Vision’ to Wilson’s Animat. Adaptive Behavior, 2(1):47–70, 1993.
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Dave Cliff and Susi Ross. Adding Temporary Memory to ZCS. Adaptive Behavior, 3(2):101–150, 1994. Also technical report: ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp347.ps.Z.
Dave Cliff and Susi Ross. Adding Temporary Memory to ZCS. Technical Report CSRP347, School of Cognitive and Computing Sciences, University of Sussex, 1995. ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp347.ps.Z.
H. G. Cobb and John J. Grefenstette. Learning the persistence of actions in reactive control rules. In Proceedings 8th International Machine Learning Workshop, pages 293–297. Morgan Kaufmann, 1991.
Philippe Collard and Cathy Escazut. Relational Schemata: A Way to Improve the Expressiveness of Classifiers. In Eshelman [186], pages 397–404.
Marco Colombetti and Marco Dorigo. Learning to Control an Autonomous Robot by Distributed Genetic Algorithms. In Roitblat and Wilson [447], pages 305–312.
Marco Colombetti and Marco Dorigo. Robot Shaping: Developing Situated Agents through Learning. Technical Report TR-92-040, International Computer Science Institute, Berkeley, CA, 1993.
Marco Colombetti and Marco Dorigo. Training Agents to Perform Sequential Behavior. Technical Report TR-93-023, International Computer Science Institute, Berkeley, CA, September 1993.
Marco Colombetti and Marco Dorigo. Training agents to perform sequential behavior. Adaptive Behavior, 2(3):247–275, 1994. ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.06-ADAP94.ps.gz.
Marco Colombetti and Marco Dorigo. Verso un’ingegneria del comportamento. Rivista di Automatica, Elettronica e Informatica, 83(10), 1996. In Italian.
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.
Marco Colombetti, Marco Dorigo, and G. Borghi. Behavior Analysis and Training: A Methodology for Behavior Engineering. IEEE Transactions on Systems, Man and Cybernetics, 26(6):365–380, 1996.
Marco Colombetti, Marco Dorigo, and G. Borghi. Robot shaping: The HAMSTER Experiment. In M. Jamshidi et al., editor, Proceedings of ISRAM’96, Sixth International Symposium on Robotics and Manufacturing, May 28–30, Montpellier, France, 1996.
M. Compiani, D. Montanari, R. Serra, and P. Simonini. Asymptotic dynamics of classifier systems. In Schaffer [463], pages 298–303.
M. Compiani, D. Montanari, R. Serra, and P. Simonini. Learning and Bucket Brigade Dynamics in Classifier Systems. In Special issue of Physica D (Vol. 42) [1], pages 202–212.
M. Compiani, D. Montanari, R. Serra, and G. Valastro. Classifier systems and neural networks. In Parallel Architectures and Neural Networks-First Italian Workshop, pages 105–118. World Scientific, Teaneck, NJ, 1989.
Clare Bates Congdon. Classification of epidemiological data: A comparison of genetic algorithm and decision tree approaches. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) [3], pages 442–449.
O. Cordón, F. Herrera, E. Herrera-Viedma, and M. Lozano. Genetic Algorithms and Fuzzy Logic in Control Processes. Technical Report DECSAI-95109, University of Granada, Granada, Spain, 1995.
Y. Davidor and H.-P. Schwefel, editors. Parallel Problem Solving From Nature-PPSN III, volume 866 of Lecture Notes in Computer Science, Berlin, 1994. Springer Verlag.
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 [463], 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 [186], pages 405–??
Lawrence Davis, Stewart W. Wilson, and David Orvosh. Temporary Memory for Examples can Speed Learning in a Simple Adaptive System. In Roitblat and Wilson [447], 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.
Michael de la Maza. A SEAGUL Visits the Race Track. In Schaffer [463], pages 208–212.
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 Koza et al. [345], 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. Cognitive Architecture and Adaptive Properties of an Motivationally Autonomous Animat. PhD thesis, Université Pierre et Marie Curie. Paris, France., 1998. Thesis is in French.
Jean-Yves Donnart and Jean-Arcady Meyer. A hierarchical classifier system implementing a motivationally autonomous animat. In Cliff et al. [118], pages 144–153.
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Jean-Yves Donnart and Jean-Arcady Meyer. Learning Reactive and Planning Rules in a Motivationally Autonomous Animat. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 26(3):381–395, 1996.
Jean-Yves Donnart and Jean-Arcady Meyer. Spatial Exploration, Map Learning, and Self-Positioning with MonaLysa. In Maes et al. [377], 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.
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Kovacs, T., Luca Lanzi, P. (2001). A Bigger Learning Classifier Systems Bibliography. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2000. Lecture Notes in Computer Science(), vol 1996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44640-0_14
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