Evolutionary Intelligence

, Volume 1, Issue 1, pp 63–82 | Cite as

Learning classifier systems: then and now

Review Article

Abstract

Broadly conceived as computational models of cognition and tools for modeling complex adaptive systems, later extended for use in adaptive robotics, and today also applied to effective classification and data-mining–what has happened to learning classifier systems in the last decade? This paper addresses this question by examining the current state of learning classifier system research.

Keywords

Genetics-based Machine Learning Learning classifier systems Classification Reinforcement learning 

References

  1. 1.
    Ahluwalia M, Bull L (2005) Proceedings of the IEEE congress on evolutionary computation, CEC 2005, 2–4 September. IEEE, EdinburghGoogle Scholar
  2. 2.
    Ahluwalia M, Bull L (1999) A genetic programming-based classifier system. In: Banzhaf W, Daida J, Eiben AE, Honavar MHGV, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, San Francisco, pp 11–18Google Scholar
  3. 3.
    Armano G (2004) Nxcs experts for financial time series forecasting. In: Bull L (ed) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, Heidelberg, pp 68–91Google Scholar
  4. 4.
    Arthur WB, Holland JH, LeBaron B, Talyer RPP (1996) Asset pricing under endogenous expectations in an artificial stock market. Tech Rep, Santa Fe Institute. This is the original version of LeBaron 1999aGoogle Scholar
  5. 5.
    Bacardit J (2004) Pittsburgh genetic-based machine learning in the data mining era: representations, generalization, and run-time. Ph.D. thesis, Enginyeria i Arquitectura La Salle, Ramon Llull University, Barcelona, European Union (Catalonia, Spain)Google Scholar
  6. 6.
    Bacardit J, Butz M (2007) Data mining in learning classifier systems: comparing xcs with gassist. In: Kovacs T, Llorà X, Takadama K, Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems: international workshops, IWLCS 2003–2005, revised selected papers, Lecture Notes in Computer Science, vol 4399, pp 282–290Google Scholar
  7. 7.
    Bacardit J, Stout M, Hirst J, Krasnogor N (2007) Data mining in proteomics with learning classifier systems. In: Kovacs T, Llorà X, Takadama X, Lanzi PL, Stolzmann W, Wilson SW (eds) In learning classifier systems: international workshops, IWLCS 2003–2005, Lecture notes in computer science, vol 4399, p 40Google Scholar
  8. 8.
    Bacardit J, Stout M, Hirst JD, Sastry K, Llorà X, Krasnogor N Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, Proceedings, London, England, July 7–11, 2007. ACM, New York, pp 346–353Google Scholar
  9. 9.
    Bagnall AJ, Zatuchna ZV (2005) On the classification of maze problems. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems. Studies in fuzziness and soft computing, vol 183. Springer, Heidelberg, pp 307–316Google Scholar
  10. 10.
    Baird LC (1995) Residual algorithms: reinforcement learning with function approximation. In: Proceedings of the twelfth international conference on machine learning. Morgan Kaufman, San Francisco, pp 30–77Google Scholar
  11. 11.
    Banzhaf W, Daida J, Eiben AE, Honavar MHGV, Jakiela M, Smith RE (eds) (1999) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, San FranciscoGoogle Scholar
  12. 12.
    Barry AM, Holmes JH, Llorà X (2004) Data mining using learning classifier systems. In: Bull L (ed) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, Heidelberg, pp 15–67Google Scholar
  13. 13.
    Bassett J, Jong KD (2000) Evolving behaviors for cooperating agents. In: Twelfth international symposium on methodologies for intelligent systems. Springer, HeidelbergGoogle Scholar
  14. 14.
    Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-based learning classifier systems: models, analysis, and applications to classification tasks. Evol Comput 11:209–238CrossRefGoogle Scholar
  15. 15.
    Bernadó-Mansilla E, Ho TK (2005) Domain of competence of xcs classifier system in complexity measurement space. IEEE Trans Evol Comput 9(1):82–104CrossRefGoogle Scholar
  16. 16.
    Bernadó-Mansilla E, Llorà X, Traus I (2005) Multiobjective learning classifier systems: an overview. Tech Rep 2005020Google Scholar
  17. 17.
    Bernadó-Mansilla E, Llorà X, Traus I (2006) Multi-objective learning classifier systems. In: Jin Y (ed) Multi-objective machine learning. Studies in computational intelligence, vol 16. Springer, Berlin, pp 261–288Google Scholar
  18. 18.
    Bertsekas DP, Tsitsiklis J (1996) Neuro-dynamic programming. Athena Scientific, BelmontGoogle Scholar
  19. 19.
    Beyer HG, O’Reilly UM (eds) (2005) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington DC, June 25–29. ACM, New YorkGoogle Scholar
  20. 20.
    Bonarini A (2000) An introduction to learning fuzzy classifier systems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems. From foundations to applications. LNAI, vol 1813. Springer, Berlin, pp 83–104Google Scholar
  21. 21.
    Bonelli P, Parodi A (1991) An efficient classifier system and its experimental comparison with two representative learning methods on three medical domains. In: Booker LB, Belew RK (eds) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San Francisco, pp 288–295Google Scholar
  22. 22.
    Bonelli P, Parodi A, Sen S, Wilson SW (1990) NEWBOOLE: a fast GBML system. In: International conference on machine learning. Morgan Kaufmann, San Mateo, pp 153–159Google Scholar
  23. 23.
    Booker LB (1989) Triggered rule discovery in classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan Kaufmann, George Mason University, pp 265–274Google Scholar
  24. 24.
    Booker LB, Belew RK (eds) (1991) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San FranciscoGoogle Scholar
  25. 25.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32MATHCrossRefGoogle Scholar
  26. 26.
    Breiman L (2002) Looking inside the black box. Wald Lecture IIGoogle Scholar
  27. 27.
    Budd A, Stone C, Masere J, Adamatzky A, DeLacyCostello B, Bull L Towards machine learning control of chemical computers. In: Adamatzky A, Teuscher C (eds) From utopian to genuine unconventional computers. Luniver Press, Beckington, pp 17–36Google Scholar
  28. 28.
    Bull L (1999) On using ZCS in a simulated continuous double-auction market. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela MJ, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, Orlando, pp 83–90, 13–17 July 1999Google Scholar
  29. 29.
    Bull L (2001) Simple markov models of the genetic algorithm in classifier systems: accuracy-based fitness. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems. Third international workshop, IWLCS 2000, Paris, France, September 15–16, 2000, Revised Papers, Lecture notes in computer science, vol 1996. Springer, Heidelberg, pp 21–28Google Scholar
  30. 30.
    Bull L Simple markov models of the genetic algorithm in classifier systems: multi-step tasks. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems. Third international workshop, IWLCS 2000, Paris, France, September 15–16, 2000, Revised Papers, Lecture notes in computer science, vol 1996. Springer, Heidelberg, pp 29–36Google Scholar
  31. 31.
    Bull L (2002) On accuracy-based fitness. Soft Comput 6(3–4):154–161MATHGoogle Scholar
  32. 32.
    Bull L (2004) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, HeidelbergGoogle Scholar
  33. 33.
    Bull L (2004) Lookahead and latent learning in a simple accuracy-based classifier system. In: Yao X, Burke EK, Lozano JA, Smith J, Guervós JJM, Bullinaria JA, Rowe JE, Tiño P, Kabán A, Schwefel HP (eds) Parallel problem solving from nature—PPSN VIII, 8th international conference, Birmingham, September 18–22, 2004, Proceedings, Lecture notes in computer science, vol 3242. Springer, Heidelberg, pp 1042–1050Google Scholar
  34. 34.
    Bull L (2005) Two simple learning classifier systems. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems. Studies in fuzziness and soft computing, vol 183. Springer, Heidelberg, pp 63–90Google Scholar
  35. 35.
    Bull L, Hurst J (2000) Self-adaptive mutation in ZCS controllers. In: Proceedings of the EvoNet Workshops—EvoRob 2000. Springer, Heidelberg, pp 339–346Google Scholar
  36. 36.
    Bull L, Hurst J, Tomlinson A (2000) Mutation in classifier system controllers. In: Meyer JA et al (ed) From animals to animats 6: proceedings of the sixth international conference on simulation of adaptive behavior, pp 460–467Google Scholar
  37. 37.
    Bull L, Kovacs T (eds) (2005) Foundations of learning classifier systems. Studies in fuzziness and soft computing, vol 183. Springer, HeidelbergGoogle Scholar
  38. 38.
    Bull L, Lanzi PL, O’Hara T (2007) Anticipation mappings for learning classifier systems. In: Proceedings of the 2007 congress on evolutionary computation (CEC2007). IEEE, SingaporeGoogle Scholar
  39. 39.
    Bull L, Mansilla EB, Holmes JH (2008) Learning classifier systems in data mining. Studies in computational intelligence. Springer, HeidelbergGoogle Scholar
  40. 40.
    Bull L, O’Hara T (2002) Accuracy-based neuro and neuro-fuzzy classifier systems. In: Langdon WB, Cantú-Paz E, Mathias KE, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke EK, Jonoska N (eds) GECCO 2002: proceedings of the genetic and evolutionary computation conference, New York, 9–13 July 2002. Morgan Kaufmann, San Francisco, pp 905–911Google Scholar
  41. 41.
    Bull L, Sha’Aban A, Tomlinson A, Addison J, Heydecker B (2004) Towards distributed adaptive control for road traffic junction signals using learning classifier systems. In: Bull L (eds) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, Heidelberg, pp 276–299Google Scholar
  42. 42.
    Bull L, Studley M, Bagnall T, Whittley I (2005) On the use of rule-sharing in learning classifier system ensembles. In: Ahluwalia M, Bull L (eds) Proceedings of the IEEE congress on evolutionary computation, CEC 2005, 2–4 September. IEEE, Edinburgh, pp 612–617Google Scholar
  43. 43.
    Bull L, Studley M, Bagnall T, Whittley I (2007) On the use of rule-sharing in learning classifier system ensembles. IEEE Trans Evol Comput 11:496–502CrossRefGoogle Scholar
  44. 44.
    Bull L, Uroukov IS (2007) Initial results from the use of learning classifier systems to control in vitro neuronal networks. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, Proceedings, London, England, 7–11 July. ACM, New York, pp 369–376Google Scholar
  45. 45.
    Butz M, Goldberg DE (2003) Generalized state values in an anticipatory learning classifier system. In: Butz M, Sigaud O, Gérard P (eds) Anticipatory behavior in adaptive learning systems, foundations, theories, and systems. Lecture notes in computer science, vol 2684. Springer, Heidelberg, pp 282–301Google Scholar
  46. 46.
    Butz M, Goldberg DE, Stolzmann W (2000) Introducing a genetic generalization pressure to the anticipatory classifier system—part 1: Theoretical approach. In: Whitley LD, Goldberg DE, Cantú-Paz E, Spector L, Parmee IC, Beyer HG (eds) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, Nevada, 8–12 July. Morgan Kaufmann, San Francisco, pp 42–49Google Scholar
  47. 47.
    Butz M, Goldberg DE, Stolzmann W (2000) Investigating generalization in the anticipatory classifier system. In: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Guervós JJM, Schwefel HP (eds) PPSN. Lecture notes in computer science, vol 1917. Springer, Heidelberg, pp 735–744Google Scholar
  48. 48.
    Butz M, Goldberg DE, Stolzmann W (2001) Probability-enhanced predictions in the anticipatory classifier system. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems. Third international workshop, IWLCS 2000, Paris, France, September 15–16, 2000, Revised Papers, Lecture notes in computer science, vol 1996. Springer, Heidelberg, pp 37–51Google Scholar
  49. 49.
    Butz M, Goldberg DG, Lanzi PL (2004) Bounding learning time in xcs. In: Genetic and evolutionary computation—GECCO 2004, LNCS. Springer, SeattleGoogle Scholar
  50. 50.
    Butz M, Goldberg DG, Lanzi PL, Sastry K (2004) Bounding the population size to ensure niche support in xcs. Tech Rep 2004033, Illinois genetic algorithms laboratory, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Avenue, Urbana, vol 61801Google Scholar
  51. 51.
    Butz M, Sastry K, Goldberg DE (2003) Tournament selection: stable fitness pressure in xcs. In: Cantú-Paz E, Foster JA, Deb K, Davis L, Roy R, O’Reilly UM, Beyer HG, Standish RK, Kendall G, Wilson SW, Harman M, Wegener J, Dasgupta D, Potter MA, Schultz AC, Dowsland KA, Jonoska N, Miller JF (eds) GECCO. Lecture Notes in Computer Science, vol 2724. Springer, Heidelberg, pp 1857–1869Google Scholar
  52. 52.
    Butz M, Sigaud O, Gérard P (eds) (2003) Anticipatory behavior in adaptive learning systems, foundations, theories, and systems. Lecture notes in computer science, vol 2684. Springer, Heidelberg Google Scholar
  53. 53.
    Butz MV (2000) Anticipatory learning classifier systems. Genetic algorithms and evolutionary computation, vol 4. Springer, HeidelbergGoogle Scholar
  54. 54.
    Butz MV (2002) An algorithmic description of ACS2. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems. LNAI, vol 2321. Springer, Berlin, pp 211–229Google Scholar
  55. 55.
    Butz MV (2003) Xcs (+ tournament selection) classifier system implementation in c, Version 1.2. Tech. Rep. 2003023, Illinois genetic algorithms laboratory, University of Illinois at Urbana-ChampaignGoogle Scholar
  56. 56.
    Butz MV (2005) Kernel-based, ellipsoidal conditions in the real-valued xcs classifier system. In: Beyer HG, O’Reilly UM (eds) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington DC, June 25–29. ACM, New York, pp 1835–1842Google Scholar
  57. 57.
    Butz MV (2006) Rule-based evolutionary online learning systems: a principled approach to LCS analysis and design. Studies in fuzziness and soft computing, vol 191. Springer, Berlin Google Scholar
  58. 58.
    Butz MV, Goldberg DE, Lanzi PL (2005) Gradient descent methods in learning classifier systems: improving xcs performance in multistep problems. IEEE Trans Evol Comput 9(5):452–473CrossRefGoogle Scholar
  59. 59.
    Butz MV, Goldberg DE, Lanzi PL, Sastry K (2007) Problem solution sustenance in xcs: Markov chain analysis of niche support distributions and the impact on computational complexity. Genet Program Evol Mach 8(1):5–37CrossRefGoogle Scholar
  60. 60.
    Butz MV, Goldberg DE, Stolzmann W (2000) Introducing a genetic generalization pressure to the anticipatory classifier system—Part 1: Theoretical approach. In: Whitely D, Goldberg D, Cantú-Paz E, Ian Parmee LS, Beyer HG (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 2000). Morgan Kaufmann, San Francisco. Also Technical Report 2000005 of the Illinois Genetic Algorithms Laboratory, pp 34–41Google Scholar
  61. 61.
    Butz MV, Goldberg DE, Stolzmann W (2000) Introducing a genetic generalization pressure to the anticipatory classifier system—Part 2: Performance analysis. In: Whitely D, Goldberg D, Cantú-Paz E, Ian Parmee LS, Beyer HG (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 2000). Morgan Kaufmann, San Francisco. Also Technical Report 2000006 of the Illinois Genetic Algorithms Laboratory, pp 42–49Google Scholar
  62. 62.
    Butz MV, Goldberg DE, Stolzmann W (2000) Investigating generalization in the anticipatory classifier system. In: Proceedings of parallel problem solving from nature (PPSN VI). Also technical report 2000014 of the Illinois Genetic Algorithms LaboratoryGoogle Scholar
  63. 63.
    Butz MV, Kovacs T, Lanzi PL, Wilson SW (2004) Toward a theory of generalization and learning in xcs. IEEE Trans Evol Comput 8(1):28–46, doi:10.1109/TEVC.2003.818194 CrossRefGoogle Scholar
  64. 64.
    Butz MV, Lanzi PL, Wilson SW Function approximation with xcs: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Trans Evol Comput (in press)Google Scholar
  65. 65.
    Butz MV, Lanzi PL, Wilson SW (2006) Hyper-ellipsoidal conditions in xcs: rotation, linear approximation, and solution structure. In: Cattolico M (ed) Genetic and evolutionary computation conference, GECCO 2006: proceedings of the 8th annual conference on genetic and evolutionary computation, Seattle, Washington, 8–12 July. ACM, New York, pp 1457–1464, http://doi.acm.org/10.1145/1143997.1144237
  66. 66.
    Butz MV, Pelikan M (2006) Studying xcs/boa learning in boolean functions: structure encoding and random boolean functions. In: Cattolico M (ed) Genetic and evolutionary computation conference, GECCO 2006, Proceedings, Seattle, Washington, 8–12 July. ACM, New York, pp 1449–1456Google Scholar
  67. 67.
    Butz MV, Pelikan M, Llorà X, Goldberg DE (2005) Extracted global structure makes local building block processing effective in xcs. In: Beyer HG, O’Reilly UM (eds) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington DC, June 25–29. ACM, New York, pp 655–662Google Scholar
  68. 68.
    Butz MV, Pelikan M, Llorà X, Goldberg DE (2006) Automated global structure extraction for effective local building block processing in xcs. Evol Comput 14(3):345–380CrossRefGoogle Scholar
  69. 69.
    Butz MV, Sastry K, Goldberg DE (2005) Strong, stable, and reliable fitness pressure in xcs due to tournament selection. Genet Program Evol Mach 6(1):53–77CrossRefGoogle Scholar
  70. 70.
    Butz MV, Sigaud O, Pezzulo G, Baldassarre G (eds) (2007) Anticipatory behavior in adaptive learning systems from brains to individual and social behavior. Lecture notes in computer science, vol 4520. Springer, Heidelberg Google Scholar
  71. 71.
    Butz MV, Wilson SW (2001) An algorithmic description of XCS. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, LNAI, vol 1996. Springer, Berlin, pp 253–272Google Scholar
  72. 72.
    Cao YJ, Ireson N, Bull L, Miles R (1999) Design of a traffic junction controller using a classifier system and fuzzy logic. In: Proceedings of the sixth international conference on computational intelligence, theory, and applications. Springer, HeidelbergGoogle Scholar
  73. 73.
    Casillas J, Carse B, Bull L (2007) Fuzzy-xcs: a michigan genetic fuzzy system. IEEE Trans Fuzzy Syst 15:536–550CrossRefGoogle Scholar
  74. 74.
    Cattolico M (ed) (2006) Genetic and evolutionary computation conference, GECCO 2006, proceedings, Seattle, Washington, 8–12 July. ACM, New YorkGoogle Scholar
  75. 75.
    Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261–283Google Scholar
  76. 76.
    Cliff D, Husbands P, Meyer JA, Wilson SW (eds) (1994) From animals to animats 3. Proceedings of the third international conferenceon simulation of adaptive behavior (SAB94). Bradford Books, MIT Press, MassachusettsGoogle Scholar
  77. 77.
    Cliff D, Ross S (1995) Adding temporary memory to ZCS. Tech. Rep. CSRP347, School of Cognitive and Computing Sciences, University of Sussex, ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp347.ps.Z
  78. 78.
    Colombetti M, Dorigo M (1994) Training agents to perform sequential behavior. Adapt Behav 2(3):247–275, ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.06-ADAP94.ps.gz
  79. 79.
    Colombetti M, Dorigo M (1999) Evolutionary computation in behavior engineering. In: Evolutionary computation: theory and applications. chap. 2, World Scientific Publishing Co., Singapore. Also Technical Report. TR/IRIDIA/1996-1, IRIDIA, Université Libre de Bruxelles, pp 37–80Google Scholar
  80. 80.
    Colombetti M, Dorigo M, Borghi G (1996) Behavior analysis and training: a methodology for behavior engineering. IEEE Trans Syst Man Cybern 26(6):365–380Google Scholar
  81. 81.
    Colombetti M, Dorigo M, Borghi G (1996) Robot shaping: the HAMSTER experiment. In: Jamshidi M et al (ed) Proceedings of ISRAM’96, sixth international symposium on robotics and manufacturing, 28–30 May, MontpellierGoogle Scholar
  82. 82.
    Dam HH, Abbass HA, Lokan C Dxcs: an xcs system for distributed data mining. In: Beyer HG, O’Reilly UM (eds) (2005) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington, 25–29 June. ACM, pp 1883–1890Google Scholar
  83. 83.
    Danek M, Smith RE (2002) Xcs applied to mapping fpga architectures. In: Langdon WB, Cantú-Paz E, Mathias KE, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke EK, Jonoska N (eds) GECCO 2002: proceedings of the genetic and evolutionary computation conference, New York, 9–13 July 2002. Morgan Kaufmann, San Francisco, pp 912–919Google Scholar
  84. 84.
    Davis MS (2000) A computational model of affect theory: simulations of reducer/augmenter and learned helplessness phenomena. Ph.D. thesis, Department of Psychology, University of MichiganGoogle Scholar
  85. 85.
    De Jong KA (1988) Learning with genetic algorithms: an overview. Mach Learn 3:121–138CrossRefGoogle Scholar
  86. 86.
    Dixon PW, Corne D, Oates MJ (2002) A ruleset reduction algorithm for the xcs learning classifier system. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, 5th international workshop, IWLCS 2002, Granada, Spain, 7–8 September, Revised Papers, Lecture notes in computer science, vol 2661. Springer, Heidelberg, pp 20–29Google Scholar
  87. 87.
    Donnart JY, Meyer JA (1994) A hierarchical classifier system implementing a motivationally autonomousanimat. In: Cliff D, Husbands P, Meyer JA, Wilson SW (eds) From animals to animats 3. Proceedings of the third international conferenceon simulation of adaptive behavior (SAB94). Bradford Books, MIT Press, Massachusetts, pp 144–153Google Scholar
  88. 88.
    Donnart JY, Meyer JA (1996) Hierarchical-map building and self-positioning with MonaLysa. Adapt Behav 5(1):29–74Google Scholar
  89. 89.
    Donnart JY, Meyer JA (1996) Spatial exploration, map learning, and self-positioning with MonaLysa. In: Maes P, Mataric MJ, Meyer JA, Wilson JPSW (eds) From animals to animats 4. Proceedings of the fourth international conferenceon simulation of adaptive behavior (SAB96). Bradford Books, MIT Press, Massachusetts, pp 204–213Google Scholar
  90. 90.
    Dorigo M (1991) Using transputers to Increase speed and flexibility of genetic-based machinelearning systems. Microprocess Microprogram 34:147–152CrossRefGoogle Scholar
  91. 91.
    Dorigo M (1995) Alecsys and the autonomouse: learning to control a real robot by distributed classifier systems. Mach Learn 19:209–240, ftp://iridia.ulb.ac.be/pub/mdorigo/journals/IJ.08-MLJ95.ps.gz
  92. 92.
    Dorigo M, Bersini H (1994) A comparison of Q-learning and classifier systems. In: Cliff D, Husbands P, Meyer JA, Wilson SW (eds) From animals to animats 3. Proceedings of the third international conferenceon simulation of adaptive behavior (SAB94). Bradford Books, MIT Press, Massachusetts, pp 248–255Google Scholar
  93. 93.
    Dorigo M, Colombetti M (1994) Robot shaping: developing autonomous agents through learning. Artif Intell 2:321–370, ftp://iridia.ulb.ac.be/pub/dorigo/journals/IJ.05-AIJ94.ps.gz
  94. 94.
    Dorigo M, Colombetti M (1997) Robot shaping, an experiment in behavior engineering. Intelligent robotics and autonomous agents. MIT Press, CambridgeGoogle Scholar
  95. 95.
    Dorigo M, Colombetti M (1998) Robot shaping: an experiment in behavior engineering. MIT Press/Bradford Books, MassachusettsGoogle Scholar
  96. 96.
    Dorigo M, Schnepf U (1993) Genetics-based machine learning and behaviour based robotics: a new synthesis. IEEE Trans Syst Man Cybern 23(1):141–154, ftp://iridia.ulb.ac.be/pub/mdorigo/journals/IJ.02-SMC93.ps.gz
  97. 97.
    Dorigo M, Sirtori E (1991) Alecsys: a parallel laboratory for learning classifier systems. In: Booker LB, Belew RK (eds) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San Mateo, pp 296–302Google Scholar
  98. 98.
    Drugowitsch J, Barry A (2005) Xcs with eligibility traces. In: Beyer HG, O’Reilly UM (eds) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington, 25–29 June. ACM, New York, pp 1851–1858Google Scholar
  99. 99.
    Drugowitsch J, Barry A (2007) Mixing independent classifiers. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, pp 1596–1603Google Scholar
  100. 100.
    Drugowitsch J, Barry A (2007) A principled foundation for lcs. In: Thierens D (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, England, 7–11 July 2007, Companion Material. ACM, New York, pp 2675–2680Google Scholar
  101. 101.
    Drugowitsch J, Barry AM (2006) A formal framework and extensions for function approximation in learning classifier systems. Tech Rep CSBU-2006-02Google Scholar
  102. 102.
    Escazut C, Fogarty TC (1997) Coevolving classifier systems to control traffic signals. In: Koza JR (ed) Late breaking papers at the 1997 genetic programming conference. Stanford Bookstore, Stanford University, USAGoogle Scholar
  103. 103.
    Ferrandi F, Lanzi PL, Sciuto D (2003) Mining interesting patterns from hardware–software codesign data with the learning classifier system XCS. In: Proceedings of the 2003 congress on evolutionary computation (CEC 2003). IEEE, Canberra, Australia, pp 1486–1492, doi:10.1109/CEC.2003.1299803
  104. 104.
    Ferrandi F, Lanzi PL, Sciuto D (2004) System level hardware–software design exploration with xcs. In: Deb K, Poli R, Banzhaf W, Beyer HG, Burke EK, Darwen PJ, Dasgupta D, Floreano D, Foster JA, Harman M, Holland O, Lanzi PL, Spector L, Tettamanzi A, Thierens D, Tyrrell AM (eds) GECCO (2), Lecture notes in computer science, vol 3103. Springer, Heidelberg, pp 763–773Google Scholar
  105. 105.
    Ferrandi F, Lanzi PL, Sciuto D, Tanelli M (2004) System-level metrics for hardware/software architectural mapping. In: DELTA, IEEE Computer Society, pp 231–236Google Scholar
  106. 106.
    Flockhart IW, Radcliffe NJ (1996) A genetic algorithm-based approach to data mining. In: KDD, pp 299–302Google Scholar
  107. 107.
    Forrest S (1991) Parallelism and programming in classifier systems. Pittman, LondonGoogle Scholar
  108. 108.
    Frey PW, Slate DJ (1991) Letter recognition using Holland-style adaptive classifiers. Mach Learn 6:161–182Google Scholar
  109. 109.
    Gandhe A, Yu SH, Mehra RK, Smith RE (2007) Fused, multi-spectral automatic target recognition with xcs. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, p 1874Google Scholar
  110. 110.
    Gérard P, Meyer JA, Sigaud O (2005) Combining latent learning with dynamic programming in the modular anticipatory classifier system. Eur J Oper Res 160(3):614–637MATHCrossRefGoogle Scholar
  111. 111.
    Gérard P, Stolzmann W, Sigaud O (2002) Yacs: a new learning classifier system using anticipation. Soft Comput 6(3–4):216–228MATHGoogle Scholar
  112. 112.
    Gershoff M (2006) An investigation of hxcs traders. Master’s thesis, School of Informatics. Master of Sciences University of Edinburgh, EdinburghGoogle Scholar
  113. 113.
    Gershoff M, Schulenburg S (2007) Collective behavior based hierarchical xcs. In: Thierens D (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, England, 7–11 July 2007, Companion Material. ACM, New York, pp 2695–2700Google Scholar
  114. 114.
    Giordana A, Neri F (1995) Search-intensive concept induction. Evol Comput 3:375–416Google Scholar
  115. 115.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingGoogle Scholar
  116. 116.
    Goldberg DE (2002) The design of innovation: lessons from and for competent genetic algorithms. Kluwer, DordrechtGoogle Scholar
  117. 117.
    Goldberg DE, Horn J, Deb K (1992) What makes a problem hard for a classifier system? In: Collected abstracts for the first international workshop on learning classifiersystem (IWLCS-92). ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html. (Also technical report 92007 Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign). Available from ENCORE (ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html) in the section on Classifier Systems
  118. 118.
    Greenyer A Coil 2000 competition: The use of a learning classifier system jxcs. Technical Report. The Database Group, Colston Tower, Colston Street, BristolGoogle Scholar
  119. 119.
    Grefenstette J, Ramsey C, Schultz A (1990) Learning sequential decision rules using simulation models and competition. Mach Learn 5:355–381Google Scholar
  120. 120.
    Grefenstette JJ (ed) (1987) Proceedings of the 2nd international conference on genetic algorithms (ICGA87). Lawrence Erlbaum Associates, CambridgeGoogle Scholar
  121. 121.
    Guervós JJM, Adamidis P, Beyer HG, Martín JLFV, Schwefel HP (eds) (2002) Parallel problem solving from Nature—PPSN VII, 7th international conference, Granada, Spain, 7–11 September, Proceedings, Lecture Notes in Computer Science, vol 2439. Springer, HeidelbergGoogle Scholar
  122. 122.
    Harik G, Lobo F, Goldberg DE (1998) The compact genetic algorithm. In: Proceedings of the IEEE international conference on evolutionary computation (also IlliGAL report No. 97006), pp 523–528Google Scholar
  123. 123.
    Harik GR, Lobo FG, Sastry K (2006) Linkage learning via probabilistic modeling in the ECGA. In: Pelikan M, Sastry K, Cantú-Paz E (eds) Scalable optimization via probabilistic modeling: from algorithms to applications, chap. 3, Springer, Berlin, pp 39–61 (also IlliGAL report No. 99010)Google Scholar
  124. 124.
    Hartley A (1999) Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments. In: Banzhaf W, Daida J, Eiben AE, Honavar MHGV, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, San Francisco, pp 266–273. ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/lcs.archive/
  125. 125.
    Haykin S (1996) Adaptive filter theory. Prentice Hall, Englewood CliffsGoogle Scholar
  126. 126.
    Holland JH (1975) Adaptation in natural and articial systems. University of Michigan Press (reprinted by the MIT Press in 1992)Google Scholar
  127. 127.
    Holland JH (1976) Adaptation. In: Rosen R, Snell F (eds) Progress in theoretical biology, vol 4. Academic Press, New York, pp 263–293Google Scholar
  128. 128.
    Holland JH (1986) A mathematical framework for studying learning in a classifier system. In: Farmer D, Lapedes A, Packard N, Wendroff B (eds) Evolution, games and learning: models for adaptation in machines and nature. North-Holland, Amsterdam, pp 307–317Google Scholar
  129. 129.
    Holland JH (1986) Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Mitchell, Michalski, Carbonell (eds) Machine learning, an artificial intelligence approach, vol II, chap. 20. Morgan Kaufmann, San Francisco, pp 593–623Google Scholar
  130. 130.
    Holland JH (1986) A mathematical framework for studying learning in classifier systems. Phys D 22:307–317MATHMathSciNetGoogle Scholar
  131. 131.
    Holland JH (1990) Concerning the emergence of tag-mediated lookahead in classifier systems. Phys D 42(Special issue):188–201CrossRefGoogle Scholar
  132. 132.
    Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge (First edition 1975: University of Michigan Press, Ann Arbor)Google Scholar
  133. 133.
    Holland JH (2005) A mathematical framework for studying learning in classifier systems. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems. Studies in fuzziness and soft computing, Springer, Heidelberg, pp 203–218Google Scholar
  134. 134.
    Holland JH, Reitman JS (1978) Cognitive systems based on adaptive algorithms Reprinted in: Evolutionary computation. The fossil record. In: David BF (ed) IEEE Press, New York 1998. ISBN:0-7803-3481-7Google Scholar
  135. 135.
    Holmes JH (1996) Evolution-assisted discovery of sentinel features in epidemiologic surveillance. Ph.D. thesis, Drexel University. http://cceb.med.upenn.edu/holmes/disstxt.ps.gz
  136. 136.
    Holmes JH (1997) Discovering risk of disease with a learning classifier system. In: Bäck T (ed) Proceedings of the 7th international conference on genetic algorithms (ICGA97). Morgan Kaufmann, San Francisco. http://cceb.med.upenn.edu/holmes/icga97.ps.gz
  137. 137.
    Holmes JH (1998) Differential negative reinforcement improves classifier system learning rate in two-class problems with unequal base rates. In: Koza JR, Banzhaf W, Chellapilla K, Dorigo KDM, Fogel DB, Garzon MH, Iba DEGH, Riolo R (eds) Genetic programming 1998: proceedings of the third annual conference. Morgan Kaufmann, San Francisco, pp 635–642. http://cceb.med.upenn.edu/holmes/gp98.ps.gz
  138. 138.
    Holmes JH (1999) Evaluating learning classifier system performance in two-choice decision tasks: an LCS metric toolkit. In: Banzhaf W, Daida J, Eiben AE, Honavar MHGV, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, San Francisco, p 789. One page poster paperGoogle Scholar
  139. 139.
    Holmes JH (2005) Detection of sentinel predictor-class associations with xcs: a sensitivity analysis. In: Rothlauf F (ed) Genetic and evolutionary computation conference, GECCO 2005, workshop proceedings, Washington, 25–26 June. ACM, New York, pp 67–71Google Scholar
  140. 140.
    Holmes JH, Bilker WB (2002) The effect of missing data on learning classifier system learning rate and classification performance. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, 5th international workshop, IWLCS 2002, Granada, Spain, 7–8 September, Revised Papers, Lecture notes in computer science, vol 2661. Springer, Hielderberg, pp 46–60Google Scholar
  141. 141.
    Holmes JH, Sager JA (2005) Rule discovery in epidemiologic surveillance data using epixcs: an evolutionary computation approach. In: Miksch S, Hunter J, Keravnou ET (eds) AIME, Lecture notes in computer science, vol 3581. Springer, Heidelberg, pp 444–452Google Scholar
  142. 142.
    Hurst J, Bull L (2002) A self-adaptive xcs. In: IWLCS’01: Revised papers from the 4th international workshop on advances in learning classifier systems. Springer, London, pp 57–73Google Scholar
  143. 143.
    Hurst J, Bull L (2003) Self-adaptation in classifier system controllers. Artif Life Robot 5:109–119CrossRefGoogle Scholar
  144. 144.
    Hurst J, Bull L (2004) A self-adaptive neural learning classifier system with constructivism for mobile robot control. In: Yao X, Burke EK, Lozano JA, Smith J, Guervós JJM, Bullinaria JA, Rowe JE, Tiño, Kabán A, Schwefel HP (eds) Parallel problem solving from nature—PPSN VIII. 8th international conference, Birmingham, 18–22 September, Proceedings, Lecture notes in computer science, vol 3242. Springer, Heidelberg, pp 942–951Google Scholar
  145. 145.
    Hurst J, Bull L, Melhuish C (2002) Tcs learning classifier system controller on a real robot. In: Guervós JJM, Adamidis P, Beyer HG, Martín JLFV, Schwefel HP (eds) Parallel problem solving from Nature—PPSN VII, 7th international conference, Granada, Spain, 7–11 September, Proceedings, Lecture Notes in Computer Science, vol 2439. Springer, Heidelberg, pp 588–600Google Scholar
  146. 146.
    Janikow C (1993) A knowledge-intensive genetic algorithm for supervised learning. Mach Learn 13:189–228CrossRefGoogle Scholar
  147. 147.
    Jong KAD, Spears WM (1991) Learning concept classification rules using genetic algorithms. In: Proceedings of the twelfth international conference on artificial intelligence IJCAI-91. Morgan Kaufmann, Sydney 2:651–656Google Scholar
  148. 148.
    Kharbat F, Bull L, Odeh M (2007) Mining breast cancer data with xcs. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, pp 2066–2073Google Scholar
  149. 149.
    Kovacs T (2000) Strength or accuracy? fitness calculation in learning classifier systems. In: Learning classifier systems, from foundations to applications, Springer, London, pp 143–160Google Scholar
  150. 150.
    Kovacs T (2000) Strength or accuracy? Fitness calculation in learning classifier systems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications, Lecture notes in computer science, vol 1813. Springer, Hielderberg, pp 143–160Google Scholar
  151. 151.
    Kovacs T (2007) The lcs bibliography. http://www.cs.bris.ac.uk/kovacs/lcs/search.html
  152. 152.
    Kovacs T, Kerber M (2000) Some dimensions of problem complexity for XCS. In: Wu AS (ed) Proceedings of the 2000 genetic and evolutionary computation conference workshop program, pp 289–292Google Scholar
  153. 153.
    Kovacs T, Kerber M What makes a problem hard for XCS? In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, LNAI, vol 1996. Springer, Berlin, pp 80–99Google Scholar
  154. 154.
    Kovacs T, Lanzi PL (2000) A learning classifier systems bibliography. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications, lecture notes in computer science, vol 1813. Springer, Heidelberg, pp 321–347Google Scholar
  155. 155.
    Landau S, Picault S, Sigaud O, Gérard P (2002) A comparison between atnosferes and xcsm. In: Langdon WB, Cantú-Paz E, Mathias KE, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke EK, Jonoska N (eds) GECCO 2002: Proceedings of the genetic and evolutionary computation conference, New York, 9–13 July. Morgan Kaufmann, San Francisco, pp 926–933Google Scholar
  156. 156.
    Landau S, Picault S, Sigaud O, Gérard P (2002) Further comparison between atnosferes and xcsm. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, 5th international workshop, IWLCS 2002, Granada, Spain, 7–8 September 2002, Revised Papers, Lecture notes in computer science, vol 2661. Springer, Heidelberg, pp 99–117Google Scholar
  157. 157.
    Langdon WB, Cantú-Paz E, Mathias KE, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke EK, Jonoska N (eds) (2002) GECCO 2002: proceedings of the genetic and evolutionary computation conference, New York. Morgan Kaufmann, San FranciscoGoogle Scholar
  158. 158.
    Lanzi PL (1998) Adding memory to xcs. In: Proceedings of the IEEE world congress on computational intelligence. The 1998 IEEE international conference on evolutionary computation, 4–9 May Anchorage (AL), IEEE Press, New York, pp 609–614Google Scholar
  159. 159.
    Lanzi PL (1998) An analysis of the memory mechanism of XCSM. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Genetic programming 1998: proceedings of the third annual conference. Morgan Kaufmann, San Francisco, pp 643–651Google Scholar
  160. 160.
    Lanzi PL (1999) Extending the representation of classifier conditions part I: from binary to messy coding. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, Orlando, pp 337–344Google Scholar
  161. 161.
    Lanzi PL (2001) Mining interesting knowledge from data with the xcs classifier system. In: Spector L, Goodman ED, Wu A, Langdon W, Voigt HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 2001). Morgan Kaufmann, San Francisco, pp 958–965Google Scholar
  162. 162.
    Lanzi PL (2001) Learning classifier systems from a reinforcement learning perspective. Soft computing—a fusion of foundations, methodologies and applications 6(3):162–170. http://link.springer.de/link/service/journals/00500/bibs/2006 003/20060162.htm
  163. 163.
    Lanzi PL (2002) The xcs libraryGoogle Scholar
  164. 164.
    Lanzi PL (2007) An analysis of generalization in xcs with symbolic conditions. In: Proceedings of the 2007 congress on evolutionary computation (CEC2007). IEEE, SingaporeGoogle Scholar
  165. 165.
    Lanzi PL, Butz MV, Goldberg DE (2007) Empirical analysis of generalization and learning in xcs with gradient descent. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, vol 2. ACM Press, London, pp 1814–1821. http://www.cs.bham.ac.uk/wbl/biblio/gecco2007/docs/p1814.pdf
  166. 166.
    Lanzi PL, Loiacono D (2006) Standard and averaging reinforcement learning in xcs. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp 1489–1496. ACM Press, New York. doi:http://doi.acm.org/10.1145/1143997.1144241
  167. 167.
    Lanzi PL, Loiacono D (2007) Classifier systems that compute action mappings. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO 2007: proceedings of the 9th annual conference on genetic and evolutionary computation, vol 2. ACM Press, London, pp 1822–1829. http://www.cs.bham.ac.uk/wbl/biblio/gecco2007/docs/p1822.pdf
  168. 168.
    Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2005) Extending XCSF beyond linear approximation. In: Genetic and evolutionary computation—GECCO 2005. ACM Press, Washington, pp 1859–1866Google Scholar
  169. 169.
    Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2006) Classifier prediction based on tile coding. In: GECCO 2006: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM Press, New York, pp 1497–1504. http://doi.acm.org/10.1145/1143997.1144242
  170. 170.
    Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2007) Generalization in the xcsf classifier system: analysis, improvement, and extension. Evol Comput J 15(2):133–168Google Scholar
  171. 171.
    Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2006) 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. ACM Press, New York, pp 1505–1512. doi:http://doi.acm.org/10.1145/1143997.1144243
  172. 172.
    Lanzi PL, Loiacono D, Zanini M (2008) Evolving classifiers ensebles part one: heterogeneous predictors. In: International workshop on learning classifier systems IWLCS-2006. Springer, Berlin (accepted)Google Scholar
  173. 173.
    Lanzi PL, Loiacono D, Zanini M (2008) Evolving classifiers ensebles part two: voting predictors. In: International workshop on learning classifier systems IWLCS-2006. Springer, Berlin (accepted)Google Scholar
  174. 174.
    Lanzi PL, Perrucci A (1999) Extending the representation of classifier conditions part II: from messy coding to S-expressions. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999), Morgan-Kaufmann, Orlando, pp 345–352Google Scholar
  175. 175.
    Lanzi PL, Stolzmann W, Wilson SW (eds) (2000) Learning classifier systems: from foundations to applications. Lecture notes in computer science, vol 1813. Springer, HeidelbergGoogle Scholar
  176. 176.
    Lanzi PL, Stolzmann W, Wilson SW (eds) (2001) Advances in learning classifier systems, LNAI, vol 1996. Springer, BerlinGoogle Scholar
  177. 177.
    Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, third international workshop, IWLCS 2000, Paris, France, 15–16 September 2000, revised papers, Lecture notes in computer science, vol 1996. Springer, HeidelbergGoogle Scholar
  178. 178.
    Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, 4th international workshop, IWLCS 2001, San Francisco, 7–8 July 2001, revised papers, Lecture notes in computer science, vol 2321. Springer, HeidelbergGoogle Scholar
  179. 179.
    Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, 5th international workshop, IWLCS 2002, Granada, Spain, 7–8 September, Revised Papers, Lecture notes in computer science, vol 2661. Springer, HeidelbergGoogle Scholar
  180. 180.
    Lanzi PL, Wilson SW (2000) Toward optimal classifier system performance in non-Markov environments. Evol Comput 8(4):393–418CrossRefGoogle Scholar
  181. 181.
    Lanzi PL, Wilson SW (2006) Using convex hulls to represent classifier conditions. In: Cattolico M (ed) Genetic and evolutionary computation conference, GECCO 2006, proceedings, Seattle, Washington, 8–12 July. ACM, pp 1481–1488Google Scholar
  182. 182.
    Lanzi PL, Wilson SW (2006) Using convex hulls to represent classifier conditions. In: Cattolico M (ed) Genetic and evolutionary computation conference, GECCO 2006, proceedings, Seattle, Washington, USA, 8–12 July. ACM Press, New York, pp 1481–1488. doi:http://doi.acm.org/10.1145/1143997.1144240
  183. 183.
    Lebaron B, Arthur WB, Palmer R (1999) The time series properties of an artificial stock market. J Econ Dyn Control 23Google Scholar
  184. 184.
    Liepins GE, Hilliard MR, Palmer M, Rangarajan G Alternatives for classifier system credit assignment. In: Proceedings of the eleventh international joint conference on artificialIntelligence (IJCAI-89), pp 756–761Google Scholar
  185. 185.
    Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New YorkGoogle Scholar
  186. 186.
    Llorà X (2002) Genetics-based machine learning using fine-grained parallelism for data mining. Ph.D. thesis, Enginyeria i Arquitectura La Salle, Ramon Llull University, Barcelona, European Union (Catalonia, Spain)Google Scholar
  187. 187.
    Llorà X, i Guiu JMG (2001) Inducing partially-defined instances with evolutionary algorithms. In: Brodley CE, Danyluk AP (eds) ICML. Morgan Kaufmann, San Francisco, pp 337–344Google Scholar
  188. 188.
    Llorà X, i Guiu JMG (2001) Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2001). Morgan-Kaufmann, San Francisco, pp 461–468Google Scholar
  189. 189.
    Llorà X, Reddy R, Matesic B, Bhargava R (2007) Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, pp 2098–2105Google Scholar
  190. 190.
    Llorà X, Sastry K, Goldberg DE (2005) Binary rule encoding schemes: a study using the compact classifier system. In: Rothlauf F (ed) Genetic and evolutionary computation conference, GECCO 2005, Workshop proceedings, Washington, 25–26 June. ACM, New York, pp 88–89Google Scholar
  191. 191.
    Llorà X, Sastry K, Goldberg DE (2005) The compact classifier system: scalability analysis and first results. Congress on evolutionary computation. In: Proceedings of the IEEE congress on evolutionary computation, CEC 2005, 2–4 September, Edinburgh, UK. IEEE, pp 596–603Google Scholar
  192. 192.
    Loiacono D, Lanzi PL (2006) Xcsf with neural prediction. In: IEEE congress on evolutionary computation. CEC 2006, pp 2270–2276Google Scholar
  193. 193.
    Loiacono D, Marelli A, Lanzi PL (2007) Support vector regression for classifier prediction. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO 2007: proceedings of the 9th annual conference on genetic and evolutionary computation, vol 2, ACM Press, London, pp 1806–1813. http://www.cs.bham.ac.uk/ wbl/biblio/gecco2007/docs/p1806.pdf
  194. 194.
    i Mansilla EB, Llorà X, i Guiu JMG (2002) Xcs and gale: a comparative study of two learning classifier systems on data mining. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, 4th international workshop, IWLCS 2001, San Francisco, 7–8 July 2001, revised papers, Lecture notes in computer science, vol 2321. Springer, Heidelberg, pp 115–132Google Scholar
  195. 195.
    Marimon R, McGrattan E, Sargent TJ (1990) Money as a medium of exchange in an economy with artificially intelligentagents. J Econ Dyn Control 14:329–373. Also technical report 89-004, Santa Fe Institute 1989Google Scholar
  196. 196.
    Mellor D (2005) A first order logic classifier system. In: Beyer HG, O’Reilly UM (eds) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington, 25–29 June. ACM, pp 1819–1826Google Scholar
  197. 197.
    Miller JH, Holland JH (1991) Artificial adaptive agents in economic theory. Am Econ Rev 81(2):365–370Google Scholar
  198. 198.
    Mitchell TM (1997) Machine learning. McGraw-Hill, New YorkGoogle Scholar
  199. 199.
    Mitlöhner J (1996) Classifier systems and economic modelling. In: APL ’96. Proceedings of the APL 96 conference on designing the future, 26(4):77–86. http://www.demon.co.uk/apl385/apl96/mitl.htm
  200. 200.
    O’Hara T, Bull L (2005) Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network. In: Press I (ed) IEEE congress on evolutionary computation, pp 2046–2052Google Scholar
  201. 201.
    O’Hara T, Bull L A memetic accuracy-based neural learning classifier system. In: Proceedings of the IEEE congress on evolutionary computation, CEC 2005, 2–4 September 2005, Edinburgh, UK. IEEE (2005), pp 2040–2045Google Scholar
  202. 202.
    Orriols-Puig A, Bernadó-Mansilla E (2006) Bounding xcs’s parameters for unbalanced datasets. In: Cattolico M (ed) Genetic and evolutionary computation conference, GECCO 2006, proceedings, Seattle, Washington, 8–12 July. ACM, pp 1561–1568Google Scholar
  203. 203.
    Orriols-Puig A, Casillas J, Bernadó-Mansilla E (2007) Fuzzy-ucs: preliminary results. In: Thierens D (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, England, 7–11 July, Companion material. ACM, New York, pp 2871–2874Google Scholar
  204. 204.
    Orriols-Puig A, Goldberg DE, Sastry K, Bernadó-Mansilla E (2007) Modeling xcs in class imbalances: population size and parameter settings. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, pp 1838–1845Google Scholar
  205. 205.
    Orriols-Puig A, Sastry K, Lanzi PL, Goldberg DE, Bernadó-Mansilla E (2007) Modeling selection pressure in xcs for proportionate and tournament selection. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, pp 1846–1853Google Scholar
  206. 206.
    Patel MJ, Dorigo M (1994) Adaptive Learning of a Robot Arm. In: Fogarty TC (ed) Evolutionary computing, AISB workshop selected papers, no. 865 in Lecture notes in computer science, Springer, Heidelberg, pp 180–194Google Scholar
  207. 207.
    Pelikan M (2005) Hierarchical bayesian optimization algorithm: toward a new generation of evolutionary algorithm. Springer, BerlinMATHGoogle Scholar
  208. 208.
    Pelikan M, Goldberg DE, Cantú-Paz E (2000) Linkage learning, estimation distribution, and Bayesian networks. Evol Comput 8(3):314–341. (Also IlliGAL Report No. 98013)Google Scholar
  209. 209.
    Pipe AG, Carse B (2000) Autonomous acquisition of fuzzy rules for mobile robot control: first results from two evolutionary computation approaches. In: Whitley LD, Goldberg DE, Cantú-Paz E, Spector L, Parmee IC, Beyer HG (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 2000), Las Vegas, Nevada, 8–12 July. Morgan-Kaufmann, Cambridge, pp 849–856Google Scholar
  210. 210.
    Pipe AG, Carse B (2002) First results from experiments in fuzzy classifier system architectures for mobile robotics. In: Guervós JJM, Adamidis P, Beyer HG, Martín JLFV, Schwefel HP (eds) Parallel problem solving from Nature—PPSN VII, 7th international conference, Granada, Spain, 7–11 September, Proceedings, Lecture notes in computer science, vol 2439. Springer, Heidelberg, pp 578–587Google Scholar
  211. 211.
    Quinlan R (1996) Learning first-order definitions of functions. J Artif Intell Res 5:139–161MATHGoogle Scholar
  212. 212.
    Quinlan RJ (1993) C4.5 Programs for machine learning. Morgan Kauffmann, Los AltosGoogle Scholar
  213. 213.
    Ravichandran B, Gandhe A, Smith RE (2005) Xcs for robust automatic target recognition. In: Beyer HG, O’Reilly UM (eds) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington, 25–29 June. ACM, New York, pp 1803–1810Google Scholar
  214. 214.
    Ravichandran B, Gandhe A, Smith RE, Mehra RK Robust automatic target recognition using learning classifier systems. Inf Fusion 8(3):252–265Google Scholar
  215. 215.
    Richards RA (1995) Zeroth-order shape optimization utilizing a learning classifier system. Ph.D. thesis, Stanford University. http://www-leland.stanford.edu/~buc/SPHINcsX/book.html. Online version available at: http://www-leland.stanford.edu/~buc/SPHINcsX/book.html
  216. 216.
    Richards RA, Sheppard SD (1992) Classifier system based structural component shape improvement utilizingI-DEAS. In: Iccon user′s conference proceeding. IcconGoogle Scholar
  217. 217.
    Richards RA, Sheppard SD (1992) Learning classifier systems in design optimization. In: Design theory and methodology 1992. The American Society of Mechanical EngineersGoogle Scholar
  218. 218.
    Richards RA, Sheppard SD (1992) Two-dimensional component shape improvement via classifier system. In: Artificial intelligence in design’92. Kluwer Academic Publishers, DordrechtGoogle Scholar
  219. 219.
    Richards RA, Sheppard SD (1996) A learning classifier system for three-dimensional shape optimization. In: Voigt HM, Ebeling W, Rechenberg I, Schwefel HP (eds) Parallel problem solving from nature—PPSN IV, LNCS, vol 1141. Springer, Berlin, pp 1032–1042Google Scholar
  220. 220.
    Richards RA, Sheppard SD (1996) Three-dimensional shape optimization utilizing a learning classifier system. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic programming 1996: proceedings of the first annual conference, MIT Press, Stanford University, USA, pp 539–546Google Scholar
  221. 221.
    Riolo RL (1987) Bucket brigade performance: I. Long sequences of classifiers. In: Grefenstette JJ (ed) Proceedings of the 2nd international conference on genetic algorithms (ICGA87). Lawrence Erlbaum Associates, Cambridge, pp 184–195Google Scholar
  222. 222.
    Riolo RL (1987) Bucket brigade performance: II. Default hierarchies. In: Grefenstette JJ (ed) Proceedings of the 2nd international conference on genetic algorithms (ICGA87). Lawrence Erlbaum Associates, Cambridge, pp 196–201Google Scholar
  223. 223.
    Riolo RL The emergence of coupled sequences of classifiers. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan-Kaufmann, George Mason University, pp 256–264Google Scholar
  224. 224.
    Riolo RL (1990) Lookahead planning and latent learning in a classifier system. In: Meyer JA, Wilson SW (eds) From animals to animats 1. Proceedings of the first international conferenceon simulation of adaptive behavior (SAB90), Bradford Books, MIT Press, Massachusetts, pp 316–326Google Scholar
  225. 225.
    Rothlauf F (ed) (2005) Genetic and evolutionary computation conference, GECCO 2005, Workshop proceedings, Washington DC, 25–26 June. ACM, New YorkGoogle Scholar
  226. 226.
    Samuel A (1959) Some studies in machine learning using the game of checkers. In: Feigenbaum EA, Feldman J (eds) Computers and thought. McGraw-Hill, New YorkGoogle Scholar
  227. 227.
    Satterfield T (1999) Bilingual selection of syntactic knowledge: extending the principles and parameters approach. Kluwer, AmsterdamGoogle Scholar
  228. 228.
    Saxon S, Barry A XCS and the Monk’s problems. In: Learning classifier systems. From foundations to applications 178:223–242Google Scholar
  229. 229.
    Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan Kaufmann, George Mason UniversityGoogle Scholar
  230. 230.
    Schulenburg S, Ross P (2000) An adaptive agent based economic model. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications, lecture notes in computer science, vol 1813. Springer, Heidelberg, pp 263–282Google Scholar
  231. 231.
    Schulenburg S, Ross P (2001) Strength and money: An lcs approach to increasing returns. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, third international workshop, IWLCS 2000, Paris, France, 15–16 September 2000, revised papers, Lecture notes in computer science, vol 1996. Springer, Heidelberg, pp 114–137Google Scholar
  232. 232.
    Schulenburg S, Ross P (2002) Explorations in lcs models of stock trading. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, 4th international workshop, IWLCS 2001, San Francisco, 7–8 July, 2001, revised papers, Lecture notes in computer science, vol 2321. Springer, Heidelberg, pp 151–180Google Scholar
  233. 233.
    Schuurmans D, Schaeffer J Representational difficulties with classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan-Kaufmann, George Mason University, San Francisco, pp 328–333. http://www.cs.ualberta.ca/jonathan/Papers/Papers/classifier. ps
  234. 234.
    Sen S (1996) Modelling human categorization by a simple classifier system. http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/p020.html. WSC1: 1st Online Workshop on Soft Computing. Aug 19–30, 1996. http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/p020.html
  235. 235.
    Shafi K, Abbass HA, Zhu W (2006) The role of early stopping and population size in xcs for intrusion detection. In: Wang TD, Li X, Chen SH, Wang X, Abbass HA, Iba H, Chen G, Yao X (eds) SEAL, Lecture notes in computer science, vol 4247. Springer, Heidelberg, pp 50–57Google Scholar
  236. 236.
    Smith RE (1994) Memory exploitation in learning classifier systems. Evol Comput 2(3):199–220Google Scholar
  237. 237.
    Smith RE, Dike BA, Mehra RK, Ravichandran B, El-Fallah A (1999) Classifier systems in combat: two-sided learning of Maneuvers for advancedfighter aircraft. In: Computer methods in applied mechanics and engineering. Elsevier, AmsterdamGoogle Scholar
  238. 238.
    Smith RE, Dike BA, Ravichandran B, Mehra RK (2000) AEF The fighter aircraft LCS: a case of different LCS goals and techniques. In: Learning classifier systems. From foundations to applications. In: Lanzi PL, Stolzmann W, Wilson SW Learning classifier systems. From foundations to applications, LNAI, vol 1813. Springer, Berlin, pp 283–300Google Scholar
  239. 239.
    Smith S (1980) A learning system based on genetic adaptive algorithms. Ph.D. thesis, Department of Computer Science, University of PittsburghGoogle Scholar
  240. 240.
    Smith S (1983) Flexible learning of problem solving heuristics through adaptive search. In: Eighth international joint conference on articial intelligence. Morgan Kaufmann, Los Altos, pp 421–425Google Scholar
  241. 241.
    Stolzmann W (1996) Learning classifier systems using the cognitive mechanism of anticipatorybehavioral control, detailed version. In: Proceedings of the first European workshop on cognitive modelling, TU, Berlin, pp 82–89. http://www.psychologie.uni-wuerzburg.de/stolzmann/
  242. 242.
    Stolzmann W (1997) Two applications of anticipatory classifier systems (ACSs). In: Proceedings of the 2nd European conference on cognitive science. Manchester, pp 68–73. http://www.psychologie.uni-wuerzburg.de/stolzmann/
  243. 243.
    Stolzmann W (1998) Anticipatory classifier systems. In: Proceedings of the third annual genetic programming conference. Morgan Kaufmann, San Francisco, pp 658–664. http://www.psychologie.uni-wuerzburg.de/stolzmann/gp-98.ps.gz
  244. 244.
    Stolzmann W (2000) An introduction to anticipatory classifier systems. In: Learning classifier systems. From Foundations to applications, In: Lanzi PL, Stolzmann W, Wilson SW Learning classifier systems. From foundations to applications, LNAI, vol 1813. Springer, Berlin, pp 175–194Google Scholar
  245. 245.
    Stolzmann W, Butz M (2000) Latent learning and action planning in robots with anticipatory classifier systems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications, Lecture notes in computer science, vol 1813. Springer, Heidelberg, pp 301–320Google Scholar
  246. 246.
    Stolzmann W, Butz MV, Hoffman J, Goldberg DE (2000) First cognitive capabilities in the anticipatory classifier system. In: From animals to animats: proceedings of the sixth international conference on simulation of adaptive behavior. MIT Press, CambridgeGoogle Scholar
  247. 247.
    Stone C, Bull L (2003) For real! xcs with continuous-valued inputs. Evol Comput 11(3):298–336CrossRefGoogle Scholar
  248. 248.
    Studley M Learning classifier systems for multi-objective robot control. Ph.D. thesis, Faculty of Computing, Engineering and Mathematics University of the West of England. Learning Classifier Systems Group Technical Report UWELCSG06-005Google Scholar
  249. 249.
    Studley M, Bull L (2005) X-tcs: accuracy-based learning classifier system robotics. In: Congress on evolutionary computation. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, 2–4 September 2005. IEEE, Edinburgh, pp 2099–2106Google Scholar
  250. 250.
    Sutton RS (1988) Learning to predict by the methods of temporal differences. Mach Learn 3:9–44Google Scholar
  251. 251.
    Sutton RS, Barto AG (1998) Reinforcement learning—an introduction. MIT Press, CambridgeGoogle Scholar
  252. 252.
    Takadama K (2004) Exploring organizational-learning oriented classifier system in real-world problems. In: Bull L (eds) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, Heidelberg, pp 182–200Google Scholar
  253. 253.
    Takadama K, Nakasuka S, Shimohara K (2002) Robustness in organizational-learning oriented classifier system. Soft Comput 6(3–4):229–239MATHGoogle Scholar
  254. 254.
    Takadama K, Terano T, Shimohara K (2001) Learning classifier systems meet multiagent environments. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, third international workshop, IWLCS 2000, Paris, France, 15–16 September 2000, revised papers, Lecture notes in computer science, vol 1996. Springer, Heidelberg, pp 192–212Google Scholar
  255. 255.
    Tesfatsion L (2003) Agent-based computational economics: modeling economies as complex adaptive systems. Inf Sci 149(4):262–268CrossRefGoogle Scholar
  256. 256.
    Tharakunnel K, Goldberg D (2002) Xcs with average reward criterion in multi-step environment. Technical report, Illinois Genetic Algorithms Laboratory—University of Illinois at Urbana-ChampaignGoogle Scholar
  257. 257.
    Thierens D (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, England, 7–11 July 2007, Companion Material. ACM, New YorkGoogle Scholar
  258. 258.
    Tomlinson A, Bull L (1998) A corporate classifier system. In: Eiben AE, Bäck T, Shoenauer M, Schwefel HP (eds) Proceedings of the fifth international conference on parallel problem solving from Nature—PPSN V, no. 1498 in LNCS. Springer, Heidelberg, pp 550–559Google Scholar
  259. 259.
    Tomlinson A, Bull L (1999) On corporate classifier systems: increasing the benefits of rule linkage. In: Banzhaf W, Daida J, Eiben AE, Honavar MHGV, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, San Francisco, pp 649–656Google Scholar
  260. 260.
    Tomlinson A, Bull L (1999) A zeroth level corporate classifier system. In: Wu AS (ed) Proceedings of the 1999 genetic and evolutionary computation conference workshop program, pp 306–313. http://www.psychologie.uni-wuerzburg.de/iwlcs-99/
  261. 261.
    Tomlinson A, Bull L (2002) An accuracy based corporate classifier system. Soft Comput 6(3–4):200–215MATHGoogle Scholar
  262. 262.
    Tran TH, Sanza C, Duthen Y, Nguyen TD (2007) Xcsf with computed continuous action. In: Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York, pp 1861–1869Google Scholar
  263. 263.
    Valenzuela-Rendón M (1991) The fuzzy classifier system: a classifier system for continuously varyingvariables. In: Booker LB, Belew RK (eds) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San Mateo, pp 346–353Google Scholar
  264. 264.
    Vargas P, Filho C, Zuben FV (2004) Application of learning classifier systems to the on-line reconfiguration of electric power distribution networks. In: Bull L (eds) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, Heidelberg, pp 260–275Google Scholar
  265. 265.
    Vriend NJ (1999) On two types of GA-learning. In: Chen S (ed) Evolutionary computation in economics and finance. Springer, HeidelbergGoogle Scholar
  266. 266.
    Vriend NJ (1999) The difference between individual and population genetic algorithms. In: Banzhaf W, Daida J, Eiben AE, Honavar MHGV, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 1999). Morgan Kaufmann, San Francisco, p. 812Google Scholar
  267. 267.
    Vriend NJ (2000) An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. J Econ Dyn Control 24:1–19MATHCrossRefMathSciNetGoogle Scholar
  268. 268.
    Wada A, Takadama K, Shimohara K (2005) Counter example for q-bucket-brigade under prediction problem. In: Rothlauf F (ed) Genetic and evolutionary computation conference, GECCO 2005, Workshop proceedings, Washington, 25–26 June. ACM, New York, pp 94–99Google Scholar
  269. 269.
    Wada A, Takadama K, Shimohara K (2005) Learning classifier system equivalent with reinforcement learning with function approximation. In: Rothlauf F (ed) Genetic and evolutionary computation conference, GECCO 2005, Workshop proceedings, Washington, 25–26 June. ACM, New York, pp 92–93Google Scholar
  270. 270.
    Wada A, Takadama K, Shimohara K, Katai O (2005) Learning classifier systems with convergence and generalization. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems. Studies in fuzziness and soft computing, Springer, Heidelberg, pp 285–304Google Scholar
  271. 271.
    Watkins C (1989) Learning from delayed reward. PhD Thesis, Cambridge University, Cambridge, EnglandGoogle Scholar
  272. 272.
    Whitley LD, Goldberg DE, Cantú-Paz E, Spector L, Parmee IC, Beyer HG (eds) (2000) Proceedings of the genetic and evolutionary computation conference (GECCO 2000). Morgan-Kaufmann, San FranciscoGoogle Scholar
  273. 273.
    Whitley LD, Goldberg DE, Cantú-Paz E, Spector L, Parmee IC, Beyer HG (eds) (2000) Proceedings of the genetic and evolutionary computation conference (GECCO 2000), Las Vegas, Nevada, 8–12 July. Morgan Kaufmann, CambridgeGoogle Scholar
  274. 274.
    Wilson SW (1987) Classifier systems and the animat problem. Mach Learn 2(3):199–228Google Scholar
  275. 275.
    Wilson SW (1994) ZCS: a zeroth level classifier system. Evol Comput 2(1):1–18. http://prediction-dynamics.com Google Scholar
  276. 276.
    Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175. http://prediction-dynamics.com/ Google Scholar
  277. 277.
    Wilson SW (1995) What is netq? http://www.eskimo.com/ wilson/netq/xcs/q.html
  278. 278.
    Wilson SW (1998) Generalization in the XCS classifier system. In: Genetic programming 1998: proceedings of the third annual conference, Morgan-Kaufmann, Cambridge, pp 665–674Google Scholar
  279. 279.
    Wilson SW (2000) Get real! xcs with continuous-valued inputs. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications, Lecture notes in computer science, vol 1813. Springer, Heidelberg, pp 209–222Google Scholar
  280. 280.
    Wilson SW (2001) Mining oblique data with xcs. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, third international workshop, IWLCS 2000, Paris, France, 15–16 September 2000, revised papers, Lecture notes in computer science, vol 1996. Springer, Heidelberg, pp 158–176Google Scholar
  281. 281.
    Wilson SW (2002) Compact rulesets from xcsi. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Advances in learning classifier systems, 4th international workshop, IWLCS 2001, San Francisco, 7–8 July 2001, revised papers, Lecture notes in computer science, vol 2321. Springer, Heidelberg, pp 197–210Google Scholar
  282. 282.
    Wilson SW (2001) Function approximation with a classifier system. In: L.S. et al (ed) Proceedings of the genetic and evolutionary computation conference (GECCO 2001). Morgan-Kaufmann, San Francisco, pp 974–981. http://www.cs.bham.ac.uk/ wbl/biblio/gecco2001/d09.pdf
  283. 283.
    Wilson SW (2001) Function approximation with a classifier system. In: Spector L, Goodman ED, Wu A, Langdon W, Hans-MichaelVoigt, Gen M, Sen S, Dorigo M, Garzon SPMH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 2001). Morgan-Kaufmann, San Francisco, pp 974–981Google Scholar
  284. 284.
    Wilson SW (2002) Classifiers that approximate functions. J Nat Comput 1(2–3):211–234MATHCrossRefGoogle Scholar
  285. 285.
    Wilson SW (2005) Three architectures for continuous action. In: Kovacs T, Llorà X, Takadama K, Lanzi PL, Stolzmann W, Wilson SW (eds) IWLCS, Lecture notes in computer science, vol 4399, Springer, Heidelberg, pp 239–257Google Scholar
  286. 286.
    Wilson SW, Goldberg DE A critical review of classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan-Kaufmann, George Mason University, San Francisco, pp 244–255. http://prediction-dynamics.com/
  287. 287.
    Yao X, Burke EK, Lozano JA, Smith J, Guervós JJM, Bullinaria JA, Rowe JE, Tiño P, Kabán A, Schwefel HP (eds) (2004) Parallel problem solving from nature—PPSN VIII. 8th International conference, Birmingham, 18–22 September, Proceedings. Lecture notes in computer science, vol 3242. Springer, HeidelbergGoogle Scholar

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© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanItaly
  2. 2.Illinois Genetic Algorithm Laboratory (IlliGAL)University of Illinois at Urbana ChampaignUrbanaUSA

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