Reusing learned functionality in XCS: Code fragments with constructed functionality and constructed features

GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

Volumn , Issue , 2014, Pages 969-976

  Alvarez, Isidro M ^a   Browne, Will N ^a   Zhang, Mengjie ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Finite state machines; Genetic programming; Learning classifier systems; Pattern recognition; Scalability; XCS

Indexed keywords

FINITE AUTOMATA; FORESTRY; GENETIC ALGORITHMS; GENETIC PROGRAMMING; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; SCALABILITY;

BUILDING BLOCKES; CLASSIFICATION ACCURACY; CODE FRAGMENTS; COMPLEX PROBLEMS; LEARNING CLASSIFIER SYSTEM; RULE SET; SEARCH SPACES; STATISTICALLY SIGNIFICANT DIFFERENCE;

LEARNING SYSTEMS;

ALGORITHMS; COMPUTER PROGRAMING; PATTERN RECOGNITION; PROBLEM SOLVING; ROBOTS;

EID: 84905639944     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2598394.2611383     Document Type: Conference Paper

References (23)

1. W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic Programming An Introduction. Morgan Kaufmann Publishers, Inc., San Francisco, California, 1998.
2. L. Bull and M. Ahluwalia. Coevolving functions in genetic programming: Classification using k-nearest-neighbor. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 2:947-952, July 1999.
3. M. V. Butz, M. Pelikan, X. Llora, and D. Goldberg. Extracted global structure makes local building block processing effective in XCS. IlliGAL Report, 2005011:1045-1052, February 2005.
4. M. V. Butz and S. W. Wilson. An algorithmic description of XCS. Soft Computing, 6:144-153, 2002.
5. M. Iqbal, W. N. Browne, and M. Zhang. Evolving optimum populations with XCS classifier systems. Soft Computing, 2013(17):503-518, September 2012.
6. M. Iqbal, W. N. Browne, and M. Zhang. XCSR with computed continuous action. Proceedings of the Australasian Joint Conference on Artificial Intelligence, pages 350-361, 2012.
7. M. Iqbal, W. N. Browne, and M. Zhang. Comparison of two methods for computing action values in XCS with code-fragment actions. GECCO'13 Companion, pages 1235-1242, July 2013.
8. M. Iqbal, W. N. Browne, and M. Zhang. Extending learning classifier system with cyclic graphs for scalability on complex, large-scale boolean problems. Proceedings of the Genetic and Evolutionary Computation Conference, pages 1045-1052, 2013.
9. M. Iqbal, W. N. Browne, and M. Zhang. Learning complex, overlapping and niche imbalance boolean problems using XCS-based classifier systems. Evolutionary Intelligence, 6(2):73-91, 2013.
10. M. Iqbal, W. N. Browne, and M. Zhang. Learning overlapping natured and niche imbalance boolean problems using XCS classifier systems. Proceedings of the IEEE Congress on Evolutionary Computation, pages 1818-1825, 2013.
11. M. Iqbal, W. N. Browne, and M. Zhang. Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Transactions on Evolutionary Computation, 2013. DOI: 10.1109/TEVC. 2013.2281537.
12. J. R. Koza. A Hierarchical Approach to Learning the Boolean Multiplexer Function. Morgan Kaufmann Publishers, Inc., 1990.
13. P. Lanzi. Extending the representation of classifier conditions part i: From binary to messy coding. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 1:337-344, July 1999.
14. P. Lanzi. XCS with stack-based genetic programming. Evolutionary Computation, 2:1186-1191, 2003.
15. P. Lanzi and A. Perrucci. Extending the representation of classifier conditions part ii: From messy coding to s-expressions. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 1:345-352, July 1999.
16. P. L. Lanzi. Learning classifier systems: Then and now. Evol. Intel., 1:63-82, 2008.
17. N. Nisan and S. Schocken. The Elements of Computing Systems: Building a Modern Computer from First Principles. MIT Press, Cambridge, Massachusetts, 2008.
18. R. J. Tocci, N. S. Widmer, and G. L. Moss. Digital Systems: Principles and Applications. Prentice Hall, Upper Saddle River, New Jersey, 2011.
19. R. J. Urbanowicz and J. H. Moore. Learning classifier systems: A complete introduction, review, and roadmap. Journal of Artificial Evolution and Applications, 2009(736398):1-25, June 2009.
20. N. Wiener. Cybernetics: or Control and Communication in the Animal and the Machine. The MIT Press, Cambridge, Massachusetts, 2006.
21. S. Wilson. A zeroth level classifier system. Evolutionary Computation, 2(1):1-18, 1994.
22. S. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2), 1995.
23. S. Wilson and O. Sigaud. Learning classifier systems: A survey. Soft Computing, 11(11):1065-1078, September 2007.