Learning classifier systems: Looking back and glimpsing ahead

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4998 LNAI, Issue , 2008, Pages 1-21

  Bacardit, Jaume ^a,d   Bernadó Mansilla, Ester ^b   Butz, Martin V ^c  

^a Nottingham University Jubilee Campus   (United Kingdom)

^b UNIVERSITAT RAMON LLULL   (Spain)

^c UNIVERSITY OF WÜRZBURG   (Germany)

^d UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; COGNITIVE SYSTEMS; DYNAMICAL SYSTEMS;

COMPUTATIONAL MODEL; DISTRIBUTED SOLUTIONS; EFFICIENCY ENHANCEMENT; EVOLUTIONARY LEARNING; INTERNATIONAL WORKSHOPS; KNOWLEDGE EXTRACTION; LEARNING CLASSIFIER SYSTEM; MACHINE LEARNING TECHNIQUES;

LEARNING SYSTEMS;

EID: 57049102927     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88138-4_1     Document Type: Conference Paper

References (122)

1. Abbass, H.A., Bacardit, J., Butz, M.V., Llora, X.: Online adaption in learning classifier systems: Stream data mining. Technical Report 2004031, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign (2004)
2. Ahluwalia, M., Bull, L.: A genetic programming-based classifier system. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 11-18. Morgan Kaufmann, San Francisco (1999)
3. Bacardit, J., Garrell, J.M.: Analysis and improvements of the adaptive discretization intervals knowledge representation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 726-738. Springer, Heidelberg (2004)
4. Bacardit, J., Goldberg, D., Butz, M., Llorà, X., Garrell, J.M.: Speeding-up pittsburgh learning classifier systems: Modeling time and accuracy. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 1021-1031. Springer, Heidelberg (2004)
5. Bacardit, J.: Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain (2004)
6. Bacardit, J., Butz, M.V.: Data mining in learning classifier systems: Comparing XCS with gassist. In: Advances at the frontier of Learning Classifier Systems, pp. 282-290. Springer, Heidelberg (2007)
7. Bacardit, J., Garrell, J.M.: Bloat control and generalization pressure using the minirmum description length principle for a Pittsburgh approach learning classifier system. In: Proceedings of the 6th International Workshop on Learning Classifier Systems. LNCS (LNAI). Springer, Heidelberg (in press, 2003)
8. Bacardit, J., Goldberg, D.E., Butz, M.V.: Improving the performance of a pittsburgh learning classifier system using a default rule. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2003. LNCS (LNAI), vol. 4399, pp. 291-307. Springer, Heidelberg (2007)
9. Bacardit, J., Krasnogor, N.: Empirical evaluation of ensemble techniques for a pittsburgh learning classifier system. In: Ninth International Workshop on Learning Classifier Systems (IWLCS 2006). LNCS (LNAI). Springer, Heidelberg (to appear, 2006)
10. Bacardit, J., Krasnogor, N.: Smart crossover operator with multiple parents for a Pittsburgh learning classifier system. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1441-1448. ACM Press, New York (2006)
11. Bacardit, J., Stout, M., Krasnogor, N., Hirst, J.D., Blazewicz, J.: Coordination number prediction using learning classifier systems: performance and interpretability. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 247-254. ACM Press, New York (2006)
12. Basu, M., Ho, T.K.E.: Data Complexity in Pattern Recognition. Springer, Heidelberg (2006)
13. Bernadó-Mansilla, E., Llorà, X., Traus, I.: Multiobjective Learning Classifier Systems. In: Multi-Objective Machine Learning. Studies in Computational Intelligence, vol. 16, pp. 261-288. Springer, Heidelberg (2006)
14. Bernadó-Mansilla, E., Garrell-Guiu, J.M.: Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Evolutionary Computation 11, 209-238 (2003)
15. Bernadó-Mansilla, E., Ho, T.K.: Domain of Competence of XCS Classifier System in Complexity Measurement Space. IEEE Transactions on Evolutionary Computation 9, 82-104 (2005)
16. Bernadó-Mansilla, E., Kam Ho, T.: On Classifier Domains of Competence. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 1, pp. 136-139 (2004)
17. Bernadó-Mansilla, 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: Fourth International Workshop on Learning Classifier Systems - IWLCS-2001, pp. 337-341 (2001)
18. Bonarini, A.: Evolutionary Learning of Fuzzy rules: competition and cooperation. In: Fuzzy Modelling: Paradigms and Practice, pp. 265-284. Kluwer Academic Press, Norwell (1996)
19. Brown, G., Kovacs, T., Marshall, J.A.R.: Ucspv: principled voting in ucs rule populations. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1774-1781. ACM Press, New York (2007)
20. Browne, W.: The development of an industrial learning classifier system for datamining in a steel hot strip mill. In: Bull, L. (ed.) Applications of Learning Classifier Systems, pp. 223-259. Springer, Heidelberg (2004)
21. Browne, W.N., Ioannides, C.: Investigating scaling of an abstracted Ics utilising ternary and s-expression alphabets. In: GECCO 2007: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pp. 2759-2764. ACM Press, New York (2007)
22. Bull, L., Hurst, J., Tomlison, A.: Self-adaptive mutation in classifier system controllers. In: Meyer, J.A., Berthoz, A., Floreano, D., Roitblatt, H., Wilson, S. (eds.) From Animals to Animats 6 - The Sixth International Conference on the Simulation of Adaptive Behaviour. MIT Press, Cambridge (2000)
23. Bull, L., Studley, M., Bagnall, A., Whittley, I.: Learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation 11, 496-502 (2007)
24. Bull, L. (ed.): Applications of Learning Classifier Systems. Springer, Heidelberg (2004)
25. Bull, L.: On lookahead and latent learning in simple LCS. In: GECCO 2007: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pp. 2633-2636. ACM, New York (2007)
26. Butz, M.V., Goldberg, D.E., Lanzi, P.L.: Computational complexity of the XCS classifier system. In: Bull, L., Kovacs, T. (eds.) Foundations of Learning Classifier Systems. Studies in Fuzziness and Soft Computing, pp. 91-126. Springer, Heidelberg (2005)
27. Butz, M.V.: Anticipatory learning classifier systems. Kluwer Academic Publishers, Boston (2002)
28. Butz, M.V.: Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 1835-1842. ACM Press, New York (2005)
29. Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design. Springer, Heidelberg (2006)
30. Butz, M.V., Goldberg, D.E.: Bounding the population size in XCS to ensure reproductive opportunities. In: Proceedings of the Fifth Genetic and Evolutionary Computation Conference (GECCO 2003), pp. 1844-1856 (2003)
31. Butz, M.V., Goldberg, D.E., Lanzi, P.L.: Gradient descent methods in learning classifier systems: Improving XCS performance in multistep problems. IEEE Transactions on Evolutionary Computation 9, 452-473 (2005)
32. Butz, M.V., Goldberg, D.E., Lanzi, P.L., Sastry, K.: Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity. Genetic Programming and Evolvable Machines 8, 5-37 (2007)
33. Butz, M.V., Hoffmann, J.: Anticipations control behavior: Animal behavior in an anticipatory learning classifier system. Adaptive Behavior 10, 75-96 (2002)
34. Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W.: Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation 8, 28-46 (2004)
35. Butz, M.V., Lanzi, P.L., Wilson, S.W.: Hyper-ellipsoidal conditions in xcs: rotation, linear approximation, and solution structure. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1457-1464. ACM Press, New York (2006)
36. Butz, M.V., Pelikan, M., Llorà, X., Goldberg, D.E.: Automated global structure extraction for effective local building block processing in XCS. Evol. Comput. 14, 345-380 (2006)
37. Butz, M.V., Sastry, K., Goldberg, D.E.: Strong, stable, and reliable fitness pressure in XCS due to tournament selection. Genetic Programming and Evolvable Machines 6, 53-77 (2005)
38. Casillas, J., Carse, B., Bull, L.: Fuzzy-xcs: A michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems 15, 536-550 (2007)
39. Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic Fuzzy Systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore (2001)
40. De Jong, K.: Learning with genetic algorithms: An overview. Mach. Learn. 3, 121-138 (1988)
41. DeJong, K.A., Spears, W.M.: Learning concept classification rules using genetic algorithms. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 651-656. Morgan Kaufmann, San Francisco (1991)
42. Dixon, P.W., Corne, D.W., Oates, M.J.: A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 133-150. Springer, Heidelberg (2002)
43. Drugowitsch, J., Barry, A.: A formal framework and extensions for function approximation in learning classifier systems. Machine Learning 70, 45-88 (2008)
44. Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Heidelberg (2002)
45. Fu, C., David, L.: A Modified Classifier System Compaction Algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 920-925. Morgan Kaufmann Publishers Inc., San Francisco (2002)
46. Gérard, P., Meyer, J.A., Sigaud, O.: Combining latent learning and dynamic programming in MACS. European Journal of Operational Research 160, 614-637 (2005)
47. Gérard, P., Sigaud, O.: Adding a generalization mechanism to YACS. In: Proceedings of the Third Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 951-957 (2001)
48. Ghahramani, Z., Wolpert, D.M.: Modular decomposition in visuomotor learning. Nature, 392-395 (1997)
49. Ghosh, A., Nath, B.: Multi-objective rule mining using genetic algorithms. Information Sciences 163, 123-133 (2004)
50. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc., Reading (1989)
51. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2002)
52. Grush, R.: The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences 27, 377-396 (2004)
53. Haruno, M., Wolpert, D.M., Kawato, M.: Hierarchical mosaic for movement generation. In: Ono, T., Matsumoto, G., Llinas, R., Berthoz, A., Norgren, R., Nishijo, H., Tamura, R. (eds.) Excepta Medica International Coungress Series, vol. 1250, pp. 575-590. Elsevier, Amsterdam (2003)
54. Dam, H.H., Lokan, C., Abbas, H.A.: Evolutionary online data mining: An investigation in a dynamic environment. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 153-178. Springer, Heidelberg (2007)
55. Ho, T.K., Basu, M.: Measuring the complexity of classification problems. In: 15th International Conference on Pattern Recognition, pp. 43-47 (2000)
56. Holland, J.H.: A cognitive system with powers of generalization and adaptation (Unpublished manuscript) (1977)
57. Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Hayes-Roth, D., Waterman, F. (eds.) Pattern-directed Inference Systems, pp. 313-329. Academic Press, New York (1978)
58. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
59. Holland, J.H.: Adaptation. In: Rosen, R., Snell, F. (eds.) Progress in theoretical biology, vol. 4, pp. 263-293. Academic Press, New York (1976)
60. Holland, J.H.: Properties of the bucket brigade algorithm. In: Proceedings of an International Conference on Genetic Algorithms and their Applications, pp. 1-7 (1985)
61. Holmes, J.H., Durbin, D.R., Winston, F.K.: The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence In Medicine 19, 53-74 (2000)
62. Hurst, J., Bull, L.: A neural learning classifier system with self-adaptive constructivism for mobile robot learning. Artificial Life 12, 1-28 (2006)
63. Ishibuchi, H., Nakashima, T., Murata, T.: Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences 136, 109-133 (2001)
64. Ishibuchi, H., Nojima, Y.: Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. International Journal of Approximate Reasoning 44, 4-31 (2007)
65. Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput. 9, 3-12 (2005)
66. Kharbat, F., Bull, L., Odeh, M.: Revisiting genetic selection in the xcs learning classifier system. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pp. 2061-2068 (2005)
67. Kovacs, T.: XCS Classifier System Reliably Evolves Accurate, Complete and Minimal Representations for Boolean Functions. In: Roy, R., Chawdhry, P., Pant, R. (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 59-68. Springer, Heidelberg (1997)
68. Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation 9, 474-488 (2005)
69. Bull, L., Studley, M., Bagnall, A.J., Whittley, I.: On the use of rule sharing in learning classifier system ensembles. In: Proceedings of the 2005 Congress on Evolutionary Computation (2005)
70. Landau, S., Picault, S., Sigaud, O., Gérard, P.: Further comparison between ATNoSFERES and XCSM. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) PWLCS 2002. LNCS (LNAI), vol. 2661, pp. 99-117. Springer, Heidelberg (2003)
71. Lanzi, P.L.: An analysis of generalization in the XCS classifier system. Evolutionary Computation 7, 125-149 (1999)
72. Lanzi, P.L.: Adaptive agents with reinforcement learning and internal memory. In: From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior, pp. 333-342 (2000)
73. Lanzi, P.L.: Learning classifier systems: then and now. Evolutionary Intelligence 1, 63-82 (2008)
74. Lanzi, P.L., Loiacono, D.: Classifier systems that compute action mappings. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, London, England, pp. 1822-1829. ACM Press, New York (2007)
75. Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Extending xcsf beyond linear approximation. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 1827-1834. ACM, New York (2005)
76. Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Classifier prediction based on tile coding. In: GECCO 2006: Genetic and Evolutionary Computation Conference, pp. 1497-1504 (2006)
77. Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Prediction update algorithms for XCSF: RLS, kalman filter, and gain adaptation. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1505-1512. ACM Press, New York (2006)
78. Lanzi, P.L., Perrucci, A.: Extending the representation of classifier conditions part II: From messy coding to s-expressions. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, vol. 1, pp. 345-352. Morgan Kaufmann, San Francisco (1999)
79. Lanzi, P.L., Rocca, S., Solari, S.: An approach to analyze the evolution of symbolic conditions in learning classifier systems. In: GECCO 2007: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pp. 2795-2800. ACM Press, New York (2007)
80. Larranaga, P., Lozano, J. (eds.): Estimation of Distribution Algorithms, A New Tool for Evolutionnary Computation. Genetic Algorithms and Evolutionnary Computation. Kluwer Academic Publishers, Dordrecht (2002)
81. Llorà, X., Priya, A., Bhargava, R.: Observer-invariant histopathology using genetics-based machine learning. Natural Computing, Special issue on Learning Classifier Systems (in press, 2008)
82. Llorà, X., Garrell, J.M.: Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Proceedings of the Third Genetic and Evolutionary Computation Conference, pp. 461-468. Morgan Kaufmann, San Francisco (2001)
83. Llorà, X., Reddy, R., Matesic, B., Bhargava, R.: Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 2098-2105. ACM Press, New York (2007)
84. Llorà, X., Sastry, K.: Fast rule matching for learning classifier systems via vector instructions. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1513-1520. ACM Press, New York (2006)
85. Llorà, X., Sastry, K., Goldberg, D.E., delaOssa, L.: The x-ary extended compact classifier system: Linkage learning in Pittsburgh LCS. In: Proceedings of the 9th International Workshop on Learning Classifier Systems - IWLCS 2006. LNCS (LNAI). Springer, Heidelberg (in press, 2006)
86. Llorà, X., Sastry, K., Yu, T.L., Goldberg, D.E.: Do not match, inherit: fitness surrogates for genetics-based machine learning techniques. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1798-1805. ACM, New York (2007)
87. Loiacono, D., Marelli, A., Lanzi, P.L.: Support vector regression for classifier prediction. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1806-1813. ACM Press, New York (2007)
88. Luca Lanzi, P., Loiacono, D.: XCSF with neural prediction. Evolutionary Computation, CEC 2006. IEEE Congress on (0-00) 2270-2276 (2006)
89. Marshall, J.A.R., Brown, G., Kovacs, T.: Bayesian estimation of rule accuracy in ucs. In: GECCO 2007: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pp. 2831-2834. ACM Press, New York (2007)
90. O'Regan, J.K., Noë, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24, 939-1031 (2001)
91. Orriols-Puig, A., Bernadó-Mansilla, E.: The Class Imbalance Problem in UCS Classifier System: Fitness Adaptation. In: Proceedings of the 2005 Congress on Evolutionary Computation, vol. 1, pp. 604-611. IEEE Computer Society Press, Los Alamitos (2005)
92. Orriols-Puig, A., Bernadó-Mansilla, E.: Bounding XCS's Parameters for Unbalanced Datasets. In: Proceedings of the 2006 Genetic and Evolutionary Computation Conference, vol. 2, pp. 1561-1568. ACM Press, New York (2006)
93. Orriols-Puig, A., Bernadó-Mansilla, E.: The Class Imbalance Problem in Learning Classifier Systems: A Preliminary Study. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2003. LNCS (LNAI), vol. 4399, pp. 164-183. Springer, Heidelberg (2007)
94. Orriols-Puig, A., Casillas, J., Bernadó-Mansilla, E.: Fuzzy-UCS: A Michigan-style Learning Fuzzy-Classifier System for Supervised Learning. IEEE Transactions on Evolutionary Computation (in press, 2008)
95. Orriols-Puig, A., Goldberg, D., Sastry, K., Bernadó-Mansilla, E.: Modeling XCS in Class Imbalances: Population Size and Parameter Settings. In: Proceedings of the 2007 Genetic and Evolutionary Computation Conference, vol. 2, pp. 1838-1845. ACM Press, New York (2007)
96. Orriols-Puig, A., Sastry, K., Lanzi, P., Goldberg, D., Bernadó-Mansilla, E.: Modeling selection pressure in XCS for proportionate and tournament selection. In: Proceedings of the 2007 Genetic and Evolutionary Computation Conference, vol. 2, pp. 1846-1853. ACM Press, New York (2007)
97. Orriols Puig, A., Bernadó-Mansilla, E.: Analysis of Reduction Algorithms in XCS Classifier System. In: Recent Advances in Artificial Intelligence Research and Development. Frontiers in Artificial Intelligence and Applications, vol. 113, pp. 383-390. IOS Press, Amsterdam (2004)
98. Orriols-Puig, A., Bernadó-Mansilla, E., Sastry, K., Goldberg, D.E.: Substructrual surrogates for learning decomposable classification problems: implementation and first results. In: GECCO 2007: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pp. 2875-2882. ACM, New York (2007)
99. Orriols-Puig, A., Sastry, K., Goldberg, D.E., Bernadó-Mansilla, E.: Substructural surrogates for learning decomposable classification problems. In: Bacardit, J., et al. (eds.) IWLCS 2006/2007. LNCS (LNAI), vol. 4998. Springer, Heidelberg (2008)
100. Parodi, A., Bonelli, P.: A new approach to fuzzy classifier systems. In: 5th International Conference on Genetic Algorithms, pp. 223-230. Morgan Kaufmann, San Francisco (1993)
101. Rivest, R.L.: Learning decision lists. Machine Learning 2, 229-246 (1987)
102. Smith, R.E., El-Fallah, A., Ravichandran, B., Mehra, R., Dike, B.A.: The fighter aircraft LCS: A real-world, machine innovation application. In: Bull, L. (ed.) Applications of Learning Classifier Systems, pp. 113-142. Springer, Heidelberg (2004)
103. Smith, R.E., Jiang, M.K.: A learning classifier system with mutual-informationbased fitness. Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (25-28 Sept. 2007) (2173)-2180
104. Smith, S.F.: A Learning System Based on Genetic Algorithms. PhD thesis, University of Pittsburgh (1980)
105. Smith, S.F.: Flexible learning of problem solving heuristics through adaptive search. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Los Altos, CA, pp. 421-425. Morgan Kaufmann, San Francisco (1983)
106. Stolzmann, W.: Anticipatory classifier systems. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 658-664 (1998)
107. Stone, C., Bull, L.: For real! XCS with continuous-valued inputs. Evolutionary Computation Journal 11, 298-336 (2003)
108. Stout, M., Bacardit, J., Hirst, J.D., Krasnogor, N.: Prediction of recursive convex hull class assignments for protein residues. Bioinformatics (in press, 2008)
109. Suzuki, T., Kodama, T., Furuhashi, T., Tsut, H.: Fuzzy modeling using genetic algorithms with fuzzy entropy as conciseness measure. Information Sciences 136, 53-67 (2001)
110. Tamee, K., Bull, L., Pinngern, O.: Towards clustering with XCS. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1854-1860. ACM Press, New York (2007)
111. Valenzuela-Rendón, M.: The fuzzy classifier system: A classifier system for continuously varying variables. In: Fourth International Conference on Genetic Algorithms (ICGA), pp. 346-353. Morgan Kaufmann, San Francisco (1991)
112. Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3, 149-175 (1995)
113. Wilson, S.W.: Knowledge growth in an artificial animal. In: Proceedings of an International Conference on Genetic Algorithms and Their Applications, pp. 16-23 (1985)
114. Wilson, S.W.: Classifier systems and the animat problem. Machine Learning 2, 199-228 (1987)
115. Wilson, S.W.: ZCS: A zeroth level classifier system. Evolutionary Computation 2, 1-18 (1994)
116. Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Booker, L., Forrest, S., Mitchell, M., Riolo, R.L. (eds.) Festschrift in Honor of John H. Holland, Center for the Study of Complex Systems, pp. 111-121 (1999)
117. Wilson, S.W.: Classifiers that approximate functions. Natural Computing: an international journal 1, 211-234 (2002)
118. Wilson, S.W.: Compact Rulesets from XCSI. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321. Springer, Heidelberg (2002)
119. Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11, 1317-1329 (1998)
120. Wyatt, D., Bull, L.: A memetic learning classifier system for describing continuous-valued problem spaces. In: Hart, W., Krasnogor, N., Smith, J. (eds.) Recent Advances in Memetic Algorithms, pp. 355-396. Springer, Heidelberg (2004)
121. Wyatt, D., Bull, L., Parmee, I.: Building Compact Rulesets for Describing Continuous-Valued Problem Spaces Using a Learning Classifier System. In: Parmee, I. (ed.) Adaptive Computing in Design and Manufacture, vol. VI, pp. 235-248. Springer, Heidelberg (2004)
122. Zatuchna, Z.V.: AgentP: A Learning Classifier System with Associative Perception in Maze Environments. PhD thesis, School of Computing Sciences, UEA (2005)