Evolutionary Intelligence

, Volume 6, Issue 2, pp 57–72 | Cite as

Self organizing classifiers: first steps in structured evolutionary machine learning

  • Danilo Vasconcellos Vargas
  • Hirotaka Takano
  • Junichi Murata
Special Issue

Abstract

Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifiers were proposed which are similar to LCSs but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However, more tests and analysis are required to verify its benefits. Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM). This algorithm is applied in challenging problems such as big, noisy as well as dynamically changing continuous input-action mazes (growing and compressing mazes are included) with good performance. Moreover, a genetic operator is proposed which utilizes the topological information of the SOM’s population structure, improving the results. Thus, the first steps in structured evolutionary machine learning are shown, nonetheless, the problems faced are more difficult than the state-of-art continuous input-action multi-step ones.

Keywords

Self organization Self organizing systems Self organizing map Learning classifier systems Reinforcement learning Structured evolutionary algorithms 

References

  1. 1.
    Alahakoon D, Halgamuge SK, Srinivasan B (2000) Dynamic self-organizing maps with controlled growth for knowledge discovery. Neural Netw IEEE Trans 11(3):601–614CrossRefGoogle Scholar
  2. 2.
    Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. Evol Comput IEEE Trans 6(5):443–462CrossRefGoogle Scholar
  3. 3.
    Alba E, Troya J (2000) Cellular evolutionary algorithms: evaluating the influence of ratio. In: Parallel problem solving from nature PPSN VI. Springer, pp 29–38Google Scholar
  4. 4.
    Belding T (1995) The distributed genetic algorithm revisited. In: Proceedings of the sixth international conference on genetic algorithms: University of Pittsburgh, July 15–19, 1995. Morgan Kaufmann Pub, p 114Google Scholar
  5. 5.
    Berglund E, Sitte J (2006) The parameterless self-organizing map algorithm. Neural Netw IEEE Trans 17(2):305–316CrossRefGoogle Scholar
  6. 6.
    Bonarini A, Bonacina C, Matteucci M (2000) Fuzzy and crisp representations of real-valued input for learning classifier systems. In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning Classifier Systems. Lecture notes in computer science, vol. 1813. Springer, Berlin, Heidelberg, pp 107–124Google Scholar
  7. 7.
    Bull L (2002) On using constructivism in neural classifier systems. In: Parallel problem solving from nature-PPSN VII. Lecture notes in computer science, vol. 2439. Springer, pp 558–567Google Scholar
  8. 8.
    Bull L, O’Hara T (2002) Accuracy-based neuro and neuro-fuzzy classifier systems. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann Publishers Inc, pp 905–911Google Scholar
  9. 9.
    Butz M, Herbort O (2008). Context-dependent predictions and cognitive arm control with XCSF. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM, pp 1357–1364Google Scholar
  10. 10.
    Butz M, Lanzi P, Wilson S (2008) Function approximation with XCS: hyperellipsoidal conditions, recursive least squares, and compaction. Evol Comput IEEE Trans 12(3):355–376CrossRefGoogle Scholar
  11. 11.
    Casillas J, Carse B, Bull L (2007) Fuzzy-XCS: a michigan genetic fuzzy system. Fuzzy Syst IEEE Trans 15(4):536–550CrossRefGoogle Scholar
  12. 12.
    Dittenbach M, Merkl D, Rauber A (2000) The growing hierarchical self-organizing map. In: Proceedings of the IEEE-INNS-ENNS international joint conference on, neural networks, 2000. IJCNN 2000, vol 6. IEEE, pp 15–19Google Scholar
  13. 13.
    García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644CrossRefMATHGoogle Scholar
  14. 14.
    Hester T, Stone P (2012) Intrinsically motivated model learning for a developing curious agent. In: The eleventh international conference on development and learning (ICDL), Nov 2012Google Scholar
  15. 15.
    Howard G, Bull L, Lanzi P (2009) Towards continuous actions in continuous space and time using self-adaptive constructivism in neural XCSF. In: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, pp 1219–1226Google Scholar
  16. 16.
    Hutchinson G (1957) Concluding remarks. In: Cold spring harbor symposia on quantitative biology 22, pp 415–427Google Scholar
  17. 17.
    Iorio A, Li X (2005) Solving rotated multi-objective optimization problems using differential evolution. AI 2004: advances in artificial intelligence, pp 861–872Google Scholar
  18. 18.
    Iqbal M, Browne WN, Zhang M (2012) Xcsr with computed continuous action. In: AI 2012: advances in artificial intelligence. Springer, pp 350–361Google Scholar
  19. 19.
    Lanzi P, Loiacono D, Wilson S, Goldberg D (2005) XCS with computed prediction in multistep environments. In: Proceedings of the 2005 conference on genetic and evolutionary computation. ACM, pp 1859–1866Google Scholar
  20. 20.
    Lanzi P, Riolo R (2000) A roadmap to the last decade of learning classifier system research (from 1989 to 1999). In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning Classifier Systems. Lecture notes in computer science, vol. 1813. Springer, Berlin, Heidelberg, pp 33–61Google Scholar
  21. 21.
    Manderick B, Spiessens P (1989) Fine-grained parallel genetic algorithms. In: ICGA’89, pp 428–433Google Scholar
  22. 22.
    Mouret J (2011) Novelty-based multiobjectivization. In: New horizons in evolutionary robotics. Studies in computational intelligence, vol 341. Springer, pp 139–154Google Scholar
  23. 23.
    Reehuis E, Olhofer M, Emmerich M, Sendhoff B, Bäck T (2013) Novelty and interestingness measures for design-space exploration. In: Proceedings of the fifteenth annual conference on genetic and evolutionary computation conference. ACM, pp 1541–1548Google Scholar
  24. 24.
    Stalph P, Butz M (2012) Learning local linear jacobians for flexible and adaptive robot arm control. Genet Program Evol Mach 13(2):137–157CrossRefGoogle Scholar
  25. 25.
    Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Tamee K, Bull L, Pinngern O (2007) Towards clustering with xcs. In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, pp 1854–1860Google Scholar
  27. 27.
    Tomassini M (2005) Spatially structured evolutionary algorithms. Springer, BerlinMATHGoogle Scholar
  28. 28.
    Tran H, Sanza C, Duthen Y, Nguyen T (2007) XCSF with computed continuous action. In: Genetic and evolutionary computation conference: proceedings of the 9 th annual conference on genetic and evolutionary computation, vol 7, pp 1861–1869Google Scholar
  29. 29.
    Urbanowicz R, Moore J (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol Appl 2009:1Google Scholar
  30. 30.
    Valenzuela-Rendón M (1991) The fuzzy classifier system: a classifier system for continuously varying variables. In: Proceedings of the fourth international conference on genetic algorithms pp 346–353, Morgan Kaufmann I, vol 991, pp 223–230Google Scholar
  31. 31.
    Vargas DV, Takano H, Murata J (2013) Self organizing classifiers and niched fitness. In: Proceeding of the fifteenth annual conference on genetic and evolutionary computation conference. ACM, pp 1109–1116Google Scholar
  32. 32.
    Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In Congress on evolutionary computation, 2004. CEC2004, IEEE, 2004, vol 2, pp 1980–1987Google Scholar
  33. 33.
    Widrow B, Hoff ME (1960) Adaptive switching circuits. In: 1960 IRE WESCON convention record, Part 4, New York. IRE, pp 96–104Google Scholar
  34. 34.
    Wilson S (1994) ZCS: a zeroth level classifier system. Evol Comput 2(1):1–18CrossRefGoogle Scholar
  35. 35.
    Wilson SW (2002) Classifiers that approximate functions. Nat Comput 1(2–3):211–234Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Danilo Vasconcellos Vargas
    • 1
  • Hirotaka Takano
    • 1
  • Junichi Murata
    • 1
  1. 1.Kyushu UniversityFukuokaJapan

Personalised recommendations