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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 1/2008

01.03.2008 | Review Article

Learning classifier systems: then and now

verfasst von: Pier Luca Lanzi

Erschienen in: Evolutionary Intelligence | Ausgabe 1/2008

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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.
Vorheriger Artikel Neuroevolution: from architectures to learning

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Literatur
1.
Ahluwalia M, Bull L (2005) Proceedings of the IEEE congress on evolutionary computation, CEC 2005, 2–4 September. IEEE, Edinburgh
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 11–18
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 68–91
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 1999a
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)
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 282–290
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 40
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 346–353
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 307–316
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 30–77
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 Francisco
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 15–67
13.
Bassett J, Jong KD (2000) Evolving behaviors for cooperating agents. In: Twelfth international symposium on methodologies for intelligent systems. Springer, Heidelberg
14.
Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-based learning classifier systems: models, analysis, and applications to classification tasks. Evol Comput 11:209–238CrossRef
15.
Bernadó-Mansilla E, Ho TK (2005) Domain of competence of xcs classifier system in complexity measurement space. IEEE Trans Evol Comput 9(1):82–104CrossRef
16.
Bernadó-Mansilla E, Llorà X, Traus I (2005) Multiobjective learning classifier systems: an overview. Tech Rep 2005020
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 261–288
18.
Bertsekas DP, Tsitsiklis J (1996) Neuro-dynamic programming. Athena Scientific, Belmont
19.
Beyer HG, O’Reilly UM (eds) (2005) Genetic and evolutionary computation conference, GECCO 2005. Proceedings, Washington DC, June 25–29. ACM, New York
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 83–104
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 288–295
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 153–159
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 265–274
24.
Booker LB, Belew RK (eds) (1991) Proceedings of the 4th international conference on genetic algorithms (ICGA91). Morgan Kaufmann, San Francisco
25.
26.
Breiman L (2002) Looking inside the black box. Wald Lecture II
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 17–36
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 1999
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 21–28
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 29–36
31.
Bull L (2002) On accuracy-based fitness. Soft Comput 6(3–4):154–161MATH
32.
Bull L (2004) Applications of learning classifier systems. Studies in fuzziness and soft computing. Springer, Heidelberg
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 1042–1050
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 63–90
35.
Bull L, Hurst J (2000) Self-adaptive mutation in ZCS controllers. In: Proceedings of the EvoNet Workshops—EvoRob 2000. Springer, Heidelberg, pp 339–346
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 460–467
37.
Bull L, Kovacs T (eds) (2005) Foundations of learning classifier systems. Studies in fuzziness and soft computing, vol 183. Springer, Heidelberg
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, Singapore
39.
Bull L, Mansilla EB, Holmes JH (2008) Learning classifier systems in data mining. Studies in computational intelligence. Springer, Heidelberg
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 905–911
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 276–299
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 612–617
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 11:496–502CrossRef
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 369–376
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 282–301
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 42–49
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 735–744
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 37–51
49.
Butz M, Goldberg DG, Lanzi PL (2004) Bounding learning time in xcs. In: Genetic and evolutionary computation—GECCO 2004, LNCS. Springer, Seattle
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 61801
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 1857–1869
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
53.
Butz MV (2000) Anticipatory learning classifier systems. Genetic algorithms and evolutionary computation, vol 4. Springer, Heidelberg
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 211–229
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-Champaign
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 1835–1842
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
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):452–473CrossRef
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):5–37CrossRef
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 34–41
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 42–49
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 Laboratory
63.
64.
Butz MV, Lanzi PL, Wilson SW Function approximation with xcs: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Trans Evol Comput (in press)
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.
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 1449–1456
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 655–662
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):345–380CrossRef
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):53–77CrossRef
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
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 253–272
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, Heidelberg
73.
Casillas J, Carse B, Bull L (2007) Fuzzy-xcs: a michigan genetic fuzzy system. IEEE Trans Fuzzy Syst 15:536–550CrossRef
74.
Cattolico M (ed) (2006) Genetic and evolutionary computation conference, GECCO 2006, proceedings, Seattle, Washington, 8–12 July. ACM, New York
75.
Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261–283
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, Massachusetts
77.
78.
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 37–80
80.
Colombetti M, Dorigo M, Borghi G (1996) Behavior analysis and training: a methodology for behavior engineering. IEEE Trans Syst Man Cybern 26(6):365–380
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, Montpellier
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 1883–1890
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 912–919
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 Michigan
85.
De Jong KA (1988) Learning with genetic algorithms: an overview. Mach Learn 3:121–138CrossRef
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 20–29
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 144–153
88.
Donnart JY, Meyer JA (1996) Hierarchical-map building and self-positioning with MonaLysa. Adapt Behav 5(1):29–74
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 204–213
90.
Dorigo M (1991) Using transputers to Increase speed and flexibility of genetic-based machinelearning systems. Microprocess Microprogram 34:147–152CrossRef
91.
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 248–255
93.
94.
Dorigo M, Colombetti M (1997) Robot shaping, an experiment in behavior engineering. Intelligent robotics and autonomous agents. MIT Press, Cambridge
95.
Dorigo M, Colombetti M (1998) Robot shaping: an experiment in behavior engineering. MIT Press/Bradford Books, Massachusetts
96.
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 296–302
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 1851–1858
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 1596–1603
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 2675–2680
101.
Drugowitsch J, Barry AM (2006) A formal framework and extensions for function approximation in learning classifier systems. Tech Rep CSBU-2006-02
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, USA
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.
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 763–773
105.
Ferrandi F, Lanzi PL, Sciuto D, Tanelli M (2004) System-level metrics for hardware/software architectural mapping. In: DELTA, IEEE Computer Society, pp 231–236
106.
Flockhart IW, Radcliffe NJ (1996) A genetic algorithm-based approach to data mining. In: KDD, pp 299–302
107.
Forrest S (1991) Parallelism and programming in classifier systems. Pittman, London
108.
Frey PW, Slate DJ (1991) Letter recognition using Holland-style adaptive classifiers. Mach Learn 6:161–182
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 1874
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):614–637MATHCrossRef
111.
Gérard P, Stolzmann W, Sigaud O (2002) Yacs: a new learning classifier system using anticipation. Soft Comput 6(3–4):216–228MATH
112.
Gershoff M (2006) An investigation of hxcs traders. Master’s thesis, School of Informatics. Master of Sciences University of Edinburgh, Edinburgh
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 2695–2700
114.
Giordana A, Neri F (1995) Search-intensive concept induction. Evol Comput 3:375–416
115.
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
116.
Goldberg DE (2002) The design of innovation: lessons from and for competent genetic algorithms. Kluwer, Dordrecht
117.
118.
Greenyer A Coil 2000 competition: The use of a learning classifier system jxcs. Technical Report. The Database Group, Colston Tower, Colston Street, Bristol
119.
Grefenstette J, Ramsey C, Schultz A (1990) Learning sequential decision rules using simulation models and competition. Mach Learn 5:355–381
120.
Grefenstette JJ (ed) (1987) Proceedings of the 2nd international conference on genetic algorithms (ICGA87). Lawrence Erlbaum Associates, Cambridge
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, Heidelberg
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 523–528
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)
124.
125.
Haykin S (1996) Adaptive filter theory. Prentice Hall, Englewood Cliffs
126.
Holland JH (1975) Adaptation in natural and articial systems. University of Michigan Press (reprinted by the MIT Press in 1992)
127.
Holland JH (1976) Adaptation. In: Rosen R, Snell F (eds) Progress in theoretical biology, vol 4. Academic Press, New York, pp 263–293
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 307–317
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 593–623
130.
Holland JH (1986) A mathematical framework for studying learning in classifier systems. Phys D 22:307–317MATHMathSciNet
131.
Holland JH (1990) Concerning the emergence of tag-mediated lookahead in classifier systems. Phys D 42(Special issue):188–201CrossRef
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)
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 203–218
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:0-7803-3481-7
135.
136.
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.
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 paper
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 67–71
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 46–60
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 444–452
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 57–73
143.
Hurst J, Bull L (2003) Self-adaptation in classifier system controllers. Artif Life Robot 5:109–119CrossRef
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 942–951
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 588–600
146.
Janikow C (1993) A knowledge-intensive genetic algorithm for supervised learning. Mach Learn 13:189–228CrossRef
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 2:651–656
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 2066–2073
149.
Kovacs T (2000) Strength or accuracy? fitness calculation in learning classifier systems. In: Learning classifier systems, from foundations to applications, Springer, London, pp 143–160
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 143–160
151.
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 289–292
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 80–99
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 321–347
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 926–933
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 99–117
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 Francisco
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 609–614
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 643–651
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 337–344
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 958–965
162.
163.
164.
Lanzi PL (2007) An analysis of generalization in xcs with symbolic conditions. In: Proceedings of the 2007 congress on evolutionary computation (CEC2007). IEEE, Singapore
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.
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.
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 1859–1866
169.
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):133–168
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.
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)
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)
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 345–352
175.
Lanzi PL, Stolzmann W, Wilson SW (eds) (2000) Learning classifier systems: from foundations to applications. Lecture notes in computer science, vol 1813. Springer, Heidelberg
176.
Lanzi PL, Stolzmann W, Wilson SW (eds) (2001) Advances in learning classifier systems, LNAI, vol 1996. Springer, Berlin
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, Heidelberg
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, Heidelberg
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, Heidelberg
180.
Lanzi PL, Wilson SW (2000) Toward optimal classifier system performance in non-Markov environments. Evol Comput 8(4):393–418CrossRef
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 1481–1488
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.
Lebaron B, Arthur WB, Palmer R (1999) The time series properties of an artificial stock market. J Econ Dyn Control 23
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 756–761
185.
Lipson H (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, 7–11 July. ACM, New York
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)
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 337–344
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 461–468
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 2098–2105
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 88–89
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 596–603
192.
Loiacono D, Lanzi PL (2006) Xcsf with neural prediction. In: IEEE congress on evolutionary computation. CEC 2006, pp 2270–2276
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.
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 115–132
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 1989
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 1819–1826
197.
Miller JH, Holland JH (1991) Artificial adaptive agents in economic theory. Am Econ Rev 81(2):365–370
198.
Mitchell TM (1997) Machine learning. McGraw-Hill, New York
199.
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 2046–2052
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 2040–2045
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 1561–1568
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 2871–2874
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 1838–1845
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 1846–1853
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 180–194
207.
Pelikan M (2005) Hierarchical bayesian optimization algorithm: toward a new generation of evolutionary algorithm. Springer, BerlinMATH
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)
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 849–856
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 578–587
211.
Quinlan R (1996) Learning first-order definitions of functions. J Artif Intell Res 5:139–161MATH
212.
Quinlan RJ (1993) C4.5 Programs for machine learning. Morgan Kauffmann, Los Altos
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 1803–1810
214.
Ravichandran B, Gandhe A, Smith RE, Mehra RK Robust automatic target recognition using learning classifier systems. Inf Fusion 8(3):252–265
215.
216.
Richards RA, Sheppard SD (1992) Classifier system based structural component shape improvement utilizingI-DEAS. In: Iccon user′s conference proceeding. Iccon
217.
Richards RA, Sheppard SD (1992) Learning classifier systems in design optimization. In: Design theory and methodology 1992. The American Society of Mechanical Engineers
218.
Richards RA, Sheppard SD (1992) Two-dimensional component shape improvement via classifier system. In: Artificial intelligence in design’92. Kluwer Academic Publishers, Dordrecht
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 1032–1042
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 539–546
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 184–195
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 196–201
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 256–264
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 316–326
225.
Rothlauf F (ed) (2005) Genetic and evolutionary computation conference, GECCO 2005, Workshop proceedings, Washington DC, 25–26 June. ACM, New York
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 York
227.
Satterfield T (1999) Bilingual selection of syntactic knowledge: extending the principles and parameters approach. Kluwer, Amsterdam
228.
Saxon S, Barry A XCS and the Monk’s problems. In: Learning classifier systems. From foundations to applications 178:223–242
229.
Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan Kaufmann, George Mason University
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 263–282
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 114–137
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 151–180
233.
234.
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 50–57
236.
Smith RE (1994) Memory exploitation in learning classifier systems. Evol Comput 2(3):199–220
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, Amsterdam
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 283–300
239.
Smith S (1980) A learning system based on genetic adaptive algorithms. Ph.D. thesis, Department of Computer Science, University of Pittsburgh
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 421–425
241.
242.
243.
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 175–194
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 301–320
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, Cambridge
247.
Stone C, Bull L (2003) For real! xcs with continuous-valued inputs. Evol Comput 11(3):298–336CrossRef
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 UWELCSG06-005
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 2099–2106
250.
Sutton RS (1988) Learning to predict by the methods of temporal differences. Mach Learn 3:9–44
251.
Sutton RS, Barto AG (1998) Reinforcement learning—an introduction. MIT Press, Cambridge
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 182–200
253.
Takadama K, Nakasuka S, Shimohara K (2002) Robustness in organizational-learning oriented classifier system. Soft Comput 6(3–4):229–239MATH
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 192–212
255.
Tesfatsion L (2003) Agent-based computational economics: modeling economies as complex adaptive systems. Inf Sci 149(4):262–268CrossRef
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-Champaign
257.
Thierens D (ed) Genetic and evolutionary computation conference, GECCO 2007, proceedings, London, England, 7–11 July 2007, Companion Material. ACM, New York
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 550–559
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 649–656
260.
261.
Tomlinson A, Bull L (2002) An accuracy based corporate classifier system. Soft Comput 6(3–4):200–215MATH
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 1861–1869
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 346–353
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 260–275
265.
Vriend NJ (1999) On two types of GA-learning. In: Chen S (ed) Evolutionary computation in economics and finance. Springer, Heidelberg
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. 812
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 24:1–19MATHCrossRefMathSciNet
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 94–99
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 92–93
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 285–304
271.
Watkins C (1989) Learning from delayed reward. PhD Thesis, Cambridge University, Cambridge, England
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 Francisco
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, Cambridge
274.
Wilson SW (1987) Classifier systems and the animat problem. Mach Learn 2(3):199–228
275.
276.
277.
278.
Wilson SW (1998) Generalization in the XCS classifier system. In: Genetic programming 1998: proceedings of the third annual conference, Morgan-Kaufmann, Cambridge, pp 665–674
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 209–222
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 158–176
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 197–210
282.
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 974–981
284.
Wilson SW (2002) Classifiers that approximate functions. J Nat Comput 1(2–3):211–234MATHCrossRef
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 239–257
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.
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, Heidelberg
Metadaten
Titel
Learning classifier systems: then and now
verfasst von
Pier Luca Lanzi
Publikationsdatum
01.03.2008
Verlag
Springer-Verlag
Erschienen in
Evolutionary Intelligence / Ausgabe 1/2008
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-007-0003-3

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