Industrial Applications of Genetic Algorithms:What Can I Do with a Learning Classifier System?

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ACKNOWLEDGMENTS

The authors wish to acknowledge the financial support from the National Science Foundation (grant ECS-9212066) and from NASA’s Graduate Student Researchers Program (contract NGT 4-52403).

The authors thank the editors for their invitation to be included in this project. Much insight was gained during the GA course at The University of Alabama and they have provided a forum to showcase the students’ projects.

Lastly, the first author acknowledges his family’s great support while staying in school longer than most students. Without their support, life would be “not so grand” and graduate school would probably have taken its toll long ago.

REFERENCES

  1 Bonarini, A. (1996) Evolutionary learning of fuzzy rules: Competition and cooperation. In W. Pedrycz, ed., Fuzzy Modeling: Paradigms and Practice, Kluwer Academic Press, Norwell, MA, pp. 265-284.
  2 Cribbs, H.B. (1995) Cooperative learning classifier systems: A neural network approach. Master Thesis (TCGA Report No. 95002). The University of Alabama: Tuscaloosa, AL.
  3 Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals & Systems, 5, 45-67.
  4 DeJong, K.A. (1975) An analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan, Ann Arbor.
  5 DeJong, K.A. & Sarma, J. (1993) Generation gaps revisited. In L.D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann: San Mateo, CA.
  6 Fahlman, S.E. & Lebiere, C. (1990) The cascade-correlation architecture. Technical Report (CMU-CS-90-100). Carnegie Mellon University: Pittsburgh, PA.
  7 Goldberg, D.E. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley: Reading, MA.
  8 Holland, J. H. & Reitmann, J. (1978) Cognitive systems based on adaptive algorithms. Pattern-Directed Inference Systems. Academic Press, Inc.: New York.
  9 Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986) Induction: Processes of inference, learning, and discovery. Cambridge: MIT Press.
  10 Horn, J., Goldberg, D.E., & Deb, K. (1994) Implicit niching in a learning classifier system: Nature’s way. Evolutionary Computation, (2) 1, pp. 37-66.
  11 Hornik, K., Stinchombe, M., & White, H. (1989) Multilayer feedforward networks are universal approximators. Neural Networks, vol. 2, pp. 359-366.
  12 Koza, J. (1992) Genetic Programming: On programming computers by the means of natural selection. MIT Press: Cambridge, MA.
  13 Minsky, M. & Papert, S. (1969) Perceptrons: An introduction to computational geometry. MIT Press: Cambridge, MA.
  14 Rosenblatt, F. (1958) The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, pp. 386-408.
  15 Rumelhart, D. E., Hinton, G. E., & Williams. R. J. (1986) Learning representation by backpropagating errors. Nature, 323, pp. 533-536.
  16 Rummery, G. A. & Niranjan, M. (1994) On-line Q-learning using connectionist systems. Cambridge University Engineering Department (Report No. CUED/F-INFENG/TR 166). Cambridge CB2 1PZ, England.
  17 Smith, S.F. (1980) A learning system based on genetic adaptive algorithms. Unpublished Doctoral Dissertation. University of Pittsburgh.
  18 Smith, S.F. (1983) Flexible learning of problem solving heuristics through adaptive search. Proceedings of the 8th International Joint Conference on Artificial Intelligence, pp. 422-425.
  19 Smith, R.E. (1991) Default hierarchy formation and memory exploitation in learning classifier systems. Ph.D. Dissertation, The University of Alabama: Tuscaloosa.
  20 Smith, R.E., Forrest, S. & Perelson, A.S. (1993) Searching for diverse cooperative populations with genetic algorithms. Evolutionary Computation, (1)3, pp. 127-149.
  21 Smith, R.E. & Cribbs, H.B. (1994) Is a learning classifier system a type of neural network? Evolutionary Computation, (2) 1, pp. 19-36.
  22 Smith, R.E. & Cribbs, H.B. (1996) Cooperative versus competitive system elements in coevolutionary systems. FROM ANIMALS TO ANIMATS 4: Proceedings of the 4th International Conference on Simulation of Adaptive Behavior.
  23 Tufts, P. (1995) Dynamic classifiers: Genetic programming and classifier systems. In AAAI Fall Symposium. AAAI.
  24 Valenzuela-Rendon, M. (1991) The Fuzzy classifier system: A classifier system for continuously varying variables. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 346-353.
  25 Watkins, J. C. H. (1989) Learning with delayed rewards. Unpublished doctoral dissertation. King’s College, London.
  26 Watkins, J.C.H. & Dayan, P. (1992) Technical Note: Q-learning. Machine Learning 8, pp. 279-292.
  27 Whitley, D. (1989) Analysis of the GENITOR Algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann: San Mateo, CA.
  28 Wilson, S.W. (1986) Classifier systems and the animat problem. The Rowland Institute of Science (Research Memo RIS No. 36r). Cambridge, MA.
  29 Wilson, S. W. (1990) Perceptron Redux: Emergence of structure. In S. Forrest (ed.) Emergent Computation: Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-Organization, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks. pp. 249-256. North-Holland: Amsterdam.
  30 Wilson, S.W. (1994) ZCS: A zeroth level classifier system. Evolutionary Computation, (2) 1, pp. 1-18.
  31 Wilson, S.W. (1995) Classifier fitness based on accuracy. Evolutionary Computation, (3) 2, pp. 149-175.

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