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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2003: Intelligent Data Engineering and Automated Learning pp 254–261Cite as

Evolution at Learning: How to Promote Generalization?

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2690))

Abstract

This paper introduces generalisation concept from machine learning research and attempts to relate it to the evolutionary research. Fundamental concepts related to computational learning and the issue of genaralisation are presented. Then some evolutionary experiments are evaluated according to how well they relate to these established concepts in traditional learning. The paper concludes with emphasizing the importance of generalisation in evolutionary learning practices.

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References

  1. Kovacs, T.: What should classifier system learn. In: Proc. of the 2001 Congress on Evolutionary Computation, pp. 775–782. IEEE, Los Alamitos (2001)

    Google Scholar 

  2. Kovacs, T.: Evolving optimal populations with XCS classifier systems. Technical Report CSRP-96-17 (1996)

    Google Scholar 

  3. Kovacs, T.: XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions. In: Roy, Chawdhry, Pant (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 59–68. Springer, London (1997)

    Google Scholar 

  4. Kovacs, T.: Strength or accuracy? fitness calculation in learning classifier systems. In: Learning Classifier Systems, pp. 143–160 (1999)

    Google Scholar 

  5. Lanzi, P.L.: A Study of the Generalization Capabilities of XCS. In: Baeck, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA 1997), pp. 418–425. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  6. Lanzi, P.L.: Adding Memory to XCS. In: Proceedings of the IEEE Conference on Evolutionary Computation (ICEC 1998). IEEE Press, Los Alamitos (1998)

    Google Scholar 

  7. Saxon, S., Barry, A.: Xcs and the monk’s problem (1999)

    Google Scholar 

  8. Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)

    Article  Google Scholar 

  9. Wilson, S.W.: Generalization in the XCS classifier system. In: Koza, J., et al. (eds.) Proceedings of the Third Annual Genetic Programming Conference, pp. 665–674. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  10. Wilson, S.W.: State of XCS classifier system research. In: Learning Classifier Systems, pp. 63–82 (1999)

    Google Scholar 

  11. Wilson, S.W.: Mining oblique data with XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 158–176. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Bersano-Begey, T.F., Daida, J.M.: A discussion on generality and robustness and a framework for fitness set construction in genetic programming to promote robustness. In: Koza, J.R. (ed.) Late Breaking Papers at the 1997 Genetic Programming Conference, July 13-16. Stanford Bookstore, pp. 11–18. Stanford University, CA (1997)

    Google Scholar 

  13. Booker, L.B.: Classifier systems that learn internal world models. Machine Learning 3(2/3), 161–192 (1988)

    Article  Google Scholar 

  14. Carbonell, J.G.: Machine Learning: paradigms and methods. The MIT Press, London (1990)

    Google Scholar 

  15. Dietterich, T.G., Kong, E.B.: Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report, Department of Computer Science, Oregon State University, Corvallis, OR (1995)

    Google Scholar 

  16. Francone, F.D., Nordin, P., Banzhaf, W.: Benchmarking the generalization capabilities of a compiling genetic programming system using sparse data sets. In: John, R., Koza, D.E., Goldberg, D.B., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, pp. 72–80. MIT Press, Cambridge (1996)

    Google Scholar 

  17. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Massachusettes (1989)

    MATH  Google Scholar 

  18. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, USA (1975)

    Google Scholar 

  19. Iba, H.: Bagging, boosting, and bloating in genetic programming. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, July 13– 17, vol. 2, pp. 1053–1060. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  20. Ito, T., Iba, H., Kimura, M.: Robustness of robot programs generated by genetic programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, July 28–31, pp. 321–326. MIT Press, Cambridge (1996)

    Google Scholar 

  21. Kearns, M.: The Computational Complexity of Machine Learning. MIT Press, Cambridge (1990)

    Google Scholar 

  22. Kearns, M., Vazirani, U.: An Introduction to Computational Learning Theory. MIT Press, Cambridge (1994)

    Google Scholar 

  23. Koza, J.: Genetic Programming:On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  24. Kuscu, I.: Evolving a generalised behavior: Artificial ant problem revisited. In: William Porto, V. (ed.) Seventh Annual Conference on Evolutionary Programming, Mission Valley Marriott, San Diego, California, USA. Springer, Heidelberg (1998)

    Google Scholar 

  25. Kuscu, I.: A genetic constructive induction model. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) International Congress on Evolutionary Computation, pp. 212–217. IEEE press, Los Alamitos (1999)

    Google Scholar 

  26. Kushchu, I.: Genetic programming and evolutionary generalisation. IEEE Transactions on Evolutionary Computation 6(5), 431–442 (2002)

    Article  Google Scholar 

  27. Parks, D.J., Vail, R.K., Harpel, E.M., Moore, F.W.: The evolution of general intelligent behavior. In: Eleventh Midwest Artificial Intelligence and Cognitive Science Conference (2000)

    Google Scholar 

  28. Langley, P.: Elements of Machine Learning. Morgan Kauffmann, San Fransisco (1996)

    Google Scholar 

  29. Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)

    Article  Google Scholar 

  30. Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning an Artificial Intelligence Approach. Morgan-Kaufmann, San Francisco (1983)

    Google Scholar 

  31. Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning an Artificial Intelligence Approach Vol II. Morgan-Kaufmann, San Francisco (1986)

    Google Scholar 

  32. Moore, F.W., Garcia, O.N.: New methodology for reducing brittleness in genetic programming. In: Pohl, E. (ed.) Proceedings of the National Aerospace and Electronics 1997 Conference (NAECON 1997). IEEE Press, Los Alamitos (1997)

    Google Scholar 

  33. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

  34. Reynolds, C.W.: An evolved, vision-based behavioral model of obstacle avoidance behaviour. In: Langton, C.G. (ed.) Artificial Life III. SFI Studies in the Sciences of Complexity, Santa Fe Institute, New Mexico, USA, June 15–19 1992, vol. XVII. Addison-Wesley, Reading (1994)

    Google Scholar 

  35. Ronge, A., Nordahl, M.G.: Genetic programs and co-evolution developing robust general purpose controllers using local mating in two dimensional population. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141. Springer, Heidelberg (1996)

    Google Scholar 

  36. Jude Shavlik, W., Thomas, G.D.: Readings in Machine Learning. Morgan Kaufmann, San Mateo (1992)

    Google Scholar 

  37. Valiant, L.G.: A theory of the learnable. Communications of ACM 27, 1134–1142 (1984)

    Article  MATH  Google Scholar 

  38. Wilson, S.W.: Classifier systems and the animat problem. Machine Learning 2(3), 199–228 (1987)

    Google Scholar 

  39. Wilson, S.W.: Generalisation in XCS. In: Fogarty, T., Venturini, G. (eds.) ICML Workshop on Evolutionary Computing and Machine Learning (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. GSIM, International University of Japan, Yamato-machi, Minami Uonuma-gun, Niigata, 949-7277, JAPAN

    Ibrahim Kuschchu

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  1. Ibrahim Kuschchu
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  2. Department of Computer Science, Hong Kong Baptist University, Hong Kong

    Yiu-ming Cheung

  3. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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Kuschchu, I. (2003). Evolution at Learning: How to Promote Generalization?. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_34

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  • DOI: https://doi.org/10.1007/978-3-540-45080-1_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40550-4

  • Online ISBN: 978-3-540-45080-1

  • eBook Packages: Springer Book Archive

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