A Learning Classiier Systems Bibliography

[1]  Chris Melhuish,et al.  Applying a Restricted Mating Policy to Determine State Space Niches Using Immediate and Delayed Reinforcement , 1994, Evolutionary Computing, AISB Workshop.

[2]  Pier Luca Lanzi.  An Analysis of the Memory Mechanism of XCSM , 2007 .

[3]  Robert G. Reynolds,et al.  Multi-Rule-Set Decision-Making Schemes for A Genetic Algorithm Learning Environment for Classification Tasks , 1995 .

[4]  Robert E. Smith,et al.  Memory Exploitation in Learning Classifier Systems , 1994, Evolutionary Computation.

[5]  John H. Holmes.  Evaluating learning classifier system performance in two-choice decision tasks: an LCS metric toolkit , 1999 .

[6]  Roberto Santana,et al.  Improving the Discovery Component of Classifier Systems by the application of Estimation of Distribution Algorithms , 2009 .

[7]  A. Engelbrecht,et al.  Searching the forest: using decision trees as building blocks for evolutionary search in classification databases , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[8]  S. W. Wilson,et al.  Toward a GA Solution to the Discovery Problem , 1992 .

[9]  John H. Holmes.  A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance , 2001, IWLCS.

[10]  J. Muruzabal,et al.  Fuzzy and probabilistic reasoning in simple learning classifier systems , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[11]  Larry Bull,et al.  A Communication Architecture for Multi-agent Learning Systems , 2000, EvoWorkshops.

[12]  Michael O. Odetayo,et al.  Genetic Algorithm for Inducing Control Rules for a Dynamic System , 1989, International Conference on Genetic Algorithms.

[13]  Robert A. Richards,et al.  A Learning Classifier System for Three-Dimensional Shape Optimization , 1996, PPSN.

[14]  Peter A. N. Bosman.  Proceedings of the Genetic and Evolutionary Computation Conference Companion , 2019, GECCO.

[15]  Marco Dorigo,et al.  Adaptive Learning of a Robot Arm , 1994, Evolutionary Computing, AISB Workshop.

[16]  Peter Ross,et al.  Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems , 2002, GECCO.

[17]  Takao Terano,et al.  Learning Classifier Systems Meet Multiagent Environments , 2000, IWLCS.

[18]  Larry Bull,et al.  TCS Learning Classifier System Controller on a Real Robot , 2002, PPSN.

[19]  J. D. Schaffer,et al.  Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition) , 1984 .

[20]  Alex Alves Freitas,et al.  A Genetic Algorithm For Discovering Interesting Fuzzy Prediction Rules: Applications To Science And Technology Data , 2002, GECCO.

[21]  Stewart W. Wilson.  Perceptron redux: emergence of structure , 1990 .

[22]  Federico Divina,et al.  Evolutionary Concept Learning , 2002, GECCO.

[23]  John H. Holmes.  Discovering Risk of Disease with a Learning Classifier System , 1997, ICGA.

[24]  Tim Kovacs,et al.  Performance and population state metrics for rule-based learning systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[25]  John J. Grefenstette,et al.  Evolutionary Algorithms for Reinforcement Learning , 1999, J. Artif. Intell. Res..

[26]  Xavier Llorà,et al.  Knowledge-independent data mining with fine-grained parallel evolutionary algorithms , 2001 .

[27]  Tim Kovacs,et al.  What Makes a Problem Hard for XCS? , 2000, IWLCS.

[28]  James A. Foster,et al.  A genetic algorithm for expert system rule generation , 2001 .

[29]  Daniele Montanari,et al.  Learning and bucket brigade dynamics in classifier systems , 1990 .

[30]  Pier Luca Lanzi,et al.  Learning classifier systems from a reinforcement learning perspective , 2002, Soft Comput..

[31]  David E. Goldberg,et al.  Reinforcement learning with classifier systems: Adaptive default hierarchy formation , 1992, Appl. Artif. Intell..

[32]  Takao Terano,et al.  Towards a multiagent design principle: analyzing an organizational-learning oriented classifer system , 2002 .

[33]  Alexandros Giagkos,et al.  From Animals to Animats 14 , 2016, Lecture Notes in Computer Science.

[34]  Shinichi Nakasuka,et al.  Robustness in organizational-learning oriented classifier system , 2002, Soft Comput..

[35]  Rick L. Riolo,et al.  Bucket Brigade Performance: II. Default Hierarchies , 1987, ICGA.

[36]  Stewart W. Wilson,et al.  An Incremental Multiplexer Problem and Its Uses in Classifier System Research , 2001, IWLCS.

[37]  T. Kovacs.  Two Views of Classi er Systems , 2002 .

[38]  Martin V. Butz,et al.  First Cognitive Capabilities in the Anticipatory Classiier System First Cognitive Capabilities in the Anticipatory Classiier System , 2007 .

[39]  Stewart W. Wilson,et al.  From Animals to Animats 5. Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior , 1997 .

[40]  Kirk Twardowski,et al.  An associative architecture for genetic algorithm-based machine learning , 1994, Computer.

[41]  Takao Terano,et al.  Multiagent reinforcement learning with organizational-learning oriented classifier system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[42]  B. G. de Boer,et al.  Classifier systems: a useful approach to machine learning? , 1994 .

[43]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[44]  T. J. Held,et al.  THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS , 2010 .