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An on-line Pittsburgh LCS for the Three-Cornered Coevolution Framework

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Abstract

The Three-Cornered Coevolution Framework describes a method that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation agent and two classification agents) learn and adapt to the changes of the problems without human involvement. Here, artificial problems can be generated in concert with classification agents in order to provide insight into their relationships. Previous work on the Two-Cornered Coevolution Framework provided foundation for implementing the system that was able to set-up the problem’s difficulty appropriately while triggering the coevolutionary process. However, the triggering process was set manually without utilising the third agent as proposed in the original framework to perform this task. Previous work on the Three-Cornered Coevolution introduced the third agent (a new classification agent) to trigger the coevolutionary process within the system, where its functionality and effect on the system requires investigation. This paper details the implementation for this case; two classification agents that use different styles of learning techniques (e.g. supervised versus reinforcement learning techniques) is adapted in the classification agents to learn the various classification problems. Ultimately, Learning Classifier System (LCS) is chosen to be implemented in the participating agents. LCS has several potential characteristics, such as interpretability, generalization capability and variations in representation, that are suitable for the system. Experiments show that the Pittsburgh-style LCS with the adaptation of Tabu Search technique in S capable to autonomously adjust the problem’s difficulty and generate a wide range of problems for classification. The adaptation of A-PLUS to an ‘on-line’ system is successful implemented. Further, the classification agents (i.e. R and I) are able to solve the classification tasks where the classification performance are varied. The Three-Cornered Coevolution Framework offers a great potential for autonomous learning and provides useful insight into coevolution learning over the standard studies of pattern recognition. The system is capable of autonomously generating various problems, learning and providing insight into each learning system’s ability by determining the problem domains where they perform relatively well. This is in contrast to humans having to determine the problem domains.

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References

  1. Bacardit J, Krasnogor N (2009) Performance and efficiency of memetic Pittsburgh learning classifier systems. Evol Comput 17(3):307–342

    Article  Google Scholar 

  2. Bäck T, Fogel DB, Michalewicz Z (2000) Evolutionary computation 1: basic algorithms and operations. Institute of Physics Publishing, Bristol and Philadelphia

  3. Bäck T, Fogel DB, Michalewicz Z (2000) Evolutionary computation 2: advanced algorithms and operations. Institute of Physics Publishing, Bristol and Philadelphia

  4. 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

    Article  Google Scholar 

  5. Bishop CM (2006) Pattern recognition and machine learning, Natural Computing Series. Springer, Berlin

    Google Scholar 

  6. Butz MV (2006) Rule-based evolutionary online learning systems: a principal approach to LCS analysis and design. Springer, Berlin

    Google Scholar 

  7. Chen VC (2004) Evaluation of Bayes, ICA, PCA and SVM methods for classification. Technical Report RTO-MP-SET-080, Radar Division, US Naval Research Laboratory, 4555 Overlook Avenue, S.W. Washington DC 20375, USA

  8. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  9. Engelbrecht AP (2005) Computational intelligence: an introduction, 2nd edn. Wiley, New York

    Google Scholar 

  10. Holland JH, Reitman JS (1978) Cognitive systems based on adaptive algorithms. In: Pattern directed inference systems. Academic Press, New York, pp 313–329

  11. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–35

    Article  Google Scholar 

  12. Jennings NR, Sycara KP, Wooldridge M (1998) A roadmap of agent research and development. Auton Agents Multi-Agent Syst 1(1):7–38

    Article  Google Scholar 

  13. Jong KAD, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13:161–188

    Google Scholar 

  14. Kharbat F, Bull L, Odeh M (2005) Revisiting genetic selection in the XCS learning classifier system. In: IEEE congress on evolutionary computation, vol 3, pp 2061–2068

  15. Kovacs T (2004) Rule fitness and pathology in learning classifier systems. Evol Comput 12(1):99–135

    Article  Google Scholar 

  16. Kukenys I, Browne WN, Zhang M (2011) Transparent, online image pattern classification using a learning classier system. In: European event on evolutionary computation in image analysis and signal processing (EvoApplications’11). Springer, Berlin

  17. Maes P (1995) Artificial life meets entertainment: lifelike autonomous agents. Commun ACM 38(11):108–114

    Article  Google Scholar 

  18. Marzukhi S, Browne WN, Zhang M (2012) Two-cornered learning classifier systems for pattern generation and classification. In: Genetic and evolutionary computation conference (GECCO’12). ACM, pp 895–902

  19. Marzukhi S, Browne WN, Zhang M (2013) Adaptive artificial datasets through learning classifier systems for classification tasks. In: International workshop in learning classifier systems (IWLCS’13). ACM, pp 1243–1250

  20. Marzukhi S, Browne WN, Zhang M (2013) Adaptive artificial datasets through learning classifier systems for classification tasks. Evol Intell 6(2):93–107

  21. Marzukhi S, Browne WN, Zhang M (2014) Three-cornered coevolution learning classifier systems for classification tasks. In: Genetic and evolutionary computation conference (GECCO’14). ACM, pp 549–556

  22. Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  23. Neshatian K (2004) Strength or accuracy: credit assignment in learning classifier system, Ph.D. thesis. Bristol University

  24. Orriols-Puig A, Bernadó-Mansilla E (2005) The class imbalance problem in UCS classifier system: a preliminary study. In: International workshop learning classifier system (IWLCS’05), pp 161–180

  25. Orriols-Puig A, Bernadó-Mansilla E (2006) A further look at UCS classifier system. In: Genetic and evolutionary computation conference (GECCO’06)

  26. Orriols-Puig A, Bernadó-Mansilla E (2007) Revisiting UCS: description, fitness sharing, and comparison with XCS. In: International workshop learning classifier system (IWLCS’07), pp 96–116

  27. Orriols-Puig A, Bernadó-Mansilla E (2009) Evolutionary rule-based systems for imbalanced data sets. Soft Comput 13(3):213–225

    Article  Google Scholar 

  28. Schürmann J (1996) Pattern classification: a unified view of statistical and neural approaches. Wiley, New York

    Google Scholar 

  29. Shafi K (2008) An online and adaptive signature-based approach for intrusion detection using learning classifier systems, Ph.D. thesis. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy

  30. Stacey A (2004) An investigation of techniques for improving the performance of the Pittsburgh LCS. Technical Report UWELCSG04-005, Department Computer Science, University of Bath

  31. Troc M, Unold O (2010) Self-adaptation of parameters in a learning classifier system ensemble machine. Appl Math Comput Sci 20(1):157–174

    MATH  Google Scholar 

  32. Unold O (2010) Self-adaptive learning classifier system. J Circuits Syst Comput 19(1):275–296

    Article  Google Scholar 

  33. Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175

    Article  Google Scholar 

  34. Wilson SW (2009) Coevolution of pattern generators and recognizers. In: International workshop learning classifier system, IWLCS 2009. ACM, New York

  35. Yang J, Xu H, Jia P (2012) Effective search for Pittsburgh learning classifier systems via estimation of distribution algorithms. Inf Sci 198:100–117

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Science and Defence Technology, Computer Science Department, National Defence University Malaysia, Kem Sungai Besi, 57000, Kuala Lumpur, Malaysia

    Syahaneim Marzukhi

  2. Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand

    Will N. Browne & Mengjie Zhang

Authors
  1. Syahaneim Marzukhi
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  2. Will N. Browne
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  3. Mengjie Zhang
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Correspondence to Syahaneim Marzukhi.

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Marzukhi, S., Browne, W.N. & Zhang, M. An on-line Pittsburgh LCS for the Three-Cornered Coevolution Framework. Evol. Intel. 8, 185–201 (2015). https://doi.org/10.1007/s12065-015-0133-y

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