Learning complex, overlapping and niche imbalance Boolean problems using XCS-based classifier systems

Evolutionary Intelligence

Volumn 6, Issue 2, 2013, Pages 73-91

  Iqbal, Muhammad ^a   Browne, Will N ^a   Zhang, Mengjie ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Code fragments; Genetic programming; Learning classifier systems; Niche imbalance; Overlapping problems; XCS; XCSCFA

Indexed keywords

CODE FRAGMENTS; LEARNING CLASSIFIER SYSTEM; NICHE IMBALANCE; OVERLAPPING PROBLEMS; XCS; XCSCFA;

LEARNING SYSTEMS;

GENETIC PROGRAMMING;

EID: 84888108997     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-013-0091-1     Document Type: Article

References (57)

1. Ahluwalia M, Bull L (1999) A genetic programming based classifier System. In proceedings of the genetic and evolutionary computation conference, pp 11-18.
2. Alfaro-Cid E, Merelo JJ, de Vega FF, Esparcia-Alcázar AI, Sharman K (2010) Bloat control operators and diversity in genetic programming: a comparative study. Evol Comput 18(2): 305-332.
3. Behdad M, Barone L, French T, Bennamoun M (2012) On XCSR for electronic fraud detection. Evol Intell 5(2): 139-150.
4. Behdad M, French T, Barone L, Bennamoun M (2012) On principal component analysis for high-dimensional XCSR. Evol Intell 5(2): 129-138.
5. Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evol Comput 11(3): 209-238.
6. Brameier M, Banzhaf W (2007) Linear genetic programming. Springer, New York.
7. Bull L (2004) Applications of learning classifier systems. Springer, New York.
8. Bull L, Kovacs T (2005) Foundations of learning classifier systems: an introduction. Springer, New York.
9. Bull L, O'Hara T (2002) Accuracy-based neuro and neuro-fuzzy classifier systems. In proceedings of the genetic and evolutionary computation conference, pp 905-911.
10. Butz MV (2006) Rule-based evolutionary online learning systems: a principal approach to LCS analysis and design. Springer, New York.
11. Butz MV, Goldberg DE, Lanzi PL (2007) Effect of pure error-based fitness in XCS. In learning classifier systems, vol 4399. Springer, New York, pp 104-114.
12. Butz MV, Goldberg DE, Tharakunnel K (2003) Analysis and improvement of fitness exploitation in XCS: bounding models, tournament selection, and bilateral accuracy. Evol Comput 11(3): 239-277.
13. 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.
14. Butz MV, Wilson SW (2002) An algorithmic description of XCS. Soft Comput 6(3-4): 144-153.
15. Dam HH, Abbass HA, Lokan C, Yao X (2008) Neural-based learning classifier systems. IEEE Trans Knowl Data Eng 20(1): 26-39.
16. Dandamudi SP (2003) Fundamentals of computer organization and design. Springer, New York.
17. Franco MA, Krasnogor N, Bacardit J (2012) Analysing BioHEL using challenging Boolean functions. Evol Intell 5(2): 87-102.
18. Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3(5): 493-530.
19. Holland JH, Booker LB, Colombetti M, Dorigo M, Goldberg DE, Forrest S, Riolo RL, Smith RE, Lanzi PL, Stolzmann W, Wilson SW (2000) What is a learning classifier system? In learning classifier systems, from foundations to applications. Springer, New York, pp 3-32.
20. Hurst J, Bull L (2006) A neural learning classifier system with self-adaptive constructivism for mobile robot control. Artif Life 12(3): 353-380.
21. Ioannides C, Barrett G, Eder K (2011) Improving XCS performance on overlapping binary problems. In proceedings of the IEEE congress on evolutionary computation, pp 1420-1427.
22. Ioannides C, Barrett G, Eder K (2011) XCS Cannot learn all Boolean functions. In proceedings of the genetic and evolutionary computation conference, pp 1283-1290.
23. Iqbal M, Browne WN, Zhang M (2012) Extracting and using building blocks of knowledge in learning classifier systems. In proceedings of the genetic and evolutionary computation conference, pp 863-870.
24. Iqbal M, Browne WN, Zhang M (2012) XCSR with computed continuous Action. In proceedings of the Australasian joint conference on artificial intelligence, pp 350-361.
25. Iqbal M, Browne WN, Zhang M (2013) Comparison of two methods for computing action values in XCS with code-fragment actions. In proceedings of the genetic and evolutionary computation conference (companion), pp 1235-1242.
26. Iqbal M, Browne WN, Zhang M (2013) Evolving optimum populations with XCS classifier systems. Soft Comput 17(3): 503-518.
27. Iqbal M, Browne WN, Zhang M (2013) Learning overlapping natured and niche imbalance Boolean problems using XCS classifier systems. In proceedings of the IEEE congress on evolutionary computation, pp 825.
28. Iqbal M, Browne WN, Zhang M (2013) Using building blocks of extracted knowledge to solve complex, large-scale Boolean problems. IEEE transactions on evolutionary computation. (to appear).
29. Iqbal M, Zhang M, Browne WN (2011) Automatically defined functions for learning classifier systems. In proceedings of the genetic and evolutionary computation conference (companion), pp 375-382.
30. De Jong KA (2006) Evolutionary computation: a unified approach. The MIT Press, Cambridge.
31. Kinzett D, Johnston M, Zhang M (2009) Numerical simplification for bloat control and analysis of building blocks in genetic programming. Evol Intell 2(4): 151-168.
32. Kovacs T (1996) Evolving optimal populations with XCS classifier systems. Technical report CSR-96-17 and CSRP-9617, University of Birmingham, UK.
33. Kovacs T (2002) What should a classifier system learn and how should we measure it? Soft Comput 6(3-4): 171-182.
34. Lanzi PL (1999) Extending the representation of classifier conditions part I: from binary to messy coding. In proceedings of the genetic and evolutionary computation conference, pp 337-344.
35. Lanzi PL (2003) XCS with stack-based genetic programming. In proceedings of the IEEE congress on evolutionary computation, pp 1186-1191.
36. Lanzi PL (2008) Learning classifier systems: then and now. Evol Intell 1(1): 63-82.
37. Lanzi PL, Loiacono D (2007) Classifier systems that compute action mappings. In proceedings of the genetic and evolutionary computation conference, pp 1822-1829.
38. Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2005) XCS with computed prediction for the learning of Boolean functions. Technical Report 2005007, Illinois Genetic Algorithms Laboratory.
39. Lanzi PL, Loiacono D, Wilson SW, Goldberg DE (2007) Generalization in the XCSF classifier system: analysis, improvement, and extension. Evol Comput 15(2): 133-168.
40. Lanzi PL, Perrucci A (1999) Extending the representation of classifier conditions part II: from messy coding to S-expressions. In proceedings of the genetic and evolutionary computation conference, pp 345-352.
41. Loiacono D, Marelli A, Lanzi PL (2007) Support vector machines for computing action mappings in learning classifier systems. In proceedings of the IEEE congress on evolutionary computation, pp 2141-2148.
42. Luke S, Panait L (2006) A comparison of bloat control methods for genetic programming. Evol Comput 14(3): 309-344.
43. Miller JF, Smith SL (2006) Redundancy and computational efficiency in cartesian genetic programming. IEEE Trans Evol Comput 10(2): 167-174.
44. Miller JF, Thomson P (2000) Cartesian genetic programming. In proceedings of the European conference on genetic programming, pp 121-132.
45. Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via http://lulu. com and freely available at http://www. gp-field-guide. org. uk. (With contributions by Koza JR).
46. Shafi K, Kovacs T, Abbass HA, Zhu W (2009) Intrusion detection with evolutionary learning classifier systems. Nat Comput 8(1): 3-27.
47. Stalph PO, Rubinsztajn J, Sigaud O, Butz MV (2012) Function approximation with LWPR and XCSF: a comparative study. Evol Intell 5(2): 103-116.
48. Urbanowicz RJ, Moore JH (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol Appl 2009(1): 1-25.
49. Vijayakumar S, Schaal S (2000) Locally weighted projection regression: an o(n) algorithm for incremental real time learning in high dimensional space. In proceedings of the international conference on machine learning, pp 1079-1086.
50. Wilson SW (1994) ZCS: A zeroth level classifier system. Evol Comput 2(1): 1-18.
51. Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput (2): 149-175.
52. Wilson SW (1998) Generalization in the XCS classifier system. In proceedings of the genetic programming conference, pp 65-674.
53. Wilson SW (2000) Get real! XCS with continuous-valued inputs. In learning classifier systems. Springer, New York, pp 209-219.
54. Wilson SW (2000) Mining oblique data with XCS. In proceedings of the genetic and evolutionary computation conference (companion), pp 158-174.
55. Wilson SW (2002) Classifiers that approximate functions. Nat Comput 1: 211-233.
56. Wilson SW (2008) Classifier conditions using gene expression programming. In learning classifier systems, Springer, New York, pp 206-217.
57. Wong P, Zhang M (2006) Algebraic simplification of GP programs during evolution. In proceedings of the genetic and evolutionary computation conference, pp 927-934.