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Balancing Parent and Offspring Selection in Genetic Programming

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Book cover AI 2009: Advances in Artificial Intelligence (AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5866))

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Abstract

In order to drive Genetic Programming (GP) search towards an optimal situation, balancing selection pressure between the parent and offspring selection phases is an important aspect and very challenging. Our previous work showed that stochastic elements cannot be removed from both parent and offspring selections and suggested that maximising diversity in parents and minimising randomness in offspring could provide significantly good performance. This paper conducts additional carefully designed experiments to further investigate how diverse the parent should be if the offspring selection pressure is intensive. This paper shows that any attempt on adding more selection pressure to the parent selection can result in lower GP performance, and the higher the parent selection pressure, the worse the GP performance. The results confirm and strengthen the finding in our previous work.

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References

  1. Koza, J.R.: Genetic Programming — On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  2. Tettamanzi, A., Tomassini, M.: Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  3. Rubinstein, R., Kroese, D.: Simulation and the Monte Carlo Method, 2nd edn. John Wiley and Sons, Chichester (2007)

    Google Scholar 

  4. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms, 69–93 (1991)

    Google Scholar 

  5. Julstrom, B.A., Robinson, D.H.: Simulating exponential normalization with weighted k-tournaments. In: Proceedings of the 2000 IEEE Congress on Evolutionary Computation, pp. 227–231. IEEE Press, Los Alamitos (2000)

    Google Scholar 

  6. Filipović, V., Kratica, J., Tošić, D., Ljubić, I.: Fine grained tournament selection for the simple plant location problem. In: 5th Online World Conference on Soft Computing Methods in Industrial Applications, pp. 152–158 (2000)

    Google Scholar 

  7. Huber, R., Schell, T.: Mixed size tournament selection. Soft Computing - A Fusion of Foundations, Methodologies and Applications 6, 449–455 (2002)

    Article  MATH  Google Scholar 

  8. Sokolov, A., Whitley, D.: Unbiased tournament selection. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 1131–1138. ACM Press, New York (2005)

    Chapter  Google Scholar 

  9. Tackett, W.A.: Recombination, selection, and the genetic construction of computer programs. PhD thesis, University of Southern California, Los Angeles, CA, USA (1994)

    Google Scholar 

  10. Lang, K.J.: Hill climbing beats genetic search on a boolean circuit synthesis of Koza’s. In: Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  11. Harries, K., Smith, P.: Exploring alternative operators and search strategies in genetic programming. In: Proceedings of the Second Annual Conference on Genetic Programming, Stanford University, CA, USA, pp. 147–155. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  12. Majeed, H., Ryan, C.: A less destructive, context-aware crossover operator for gp. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 36–48. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Xie, H., Zhang, M., Andreae, P.: An analysis of constructive crossover and selection pressure in genetic programming. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 1739–1746 (2007)

    Google Scholar 

  14. Mahfoud, S.W.: Crowding and preselection revisited. In: Männer, R., Manderick, B. (eds.) Parallel problem solving from nature, vol. 2, pp. 27–36. North-Holland, Amsterdam (1992)

    Google Scholar 

  15. Terrio, M.D., Heywood, M.I.: On naive crossover biases with reproduction for simple solutions to classification problems. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 678–689. Springer, Heidelberg (2004)

    Google Scholar 

  16. Poli, R., Langdon, W.B.: Backward-chaining evolutionary algorithms. Artificial Intelligence 170, 953–982 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  17. Xie, H., Zhang, M., Andreae, P., Johnston, M.: Is the not-sampled issue in tournament selection critical? In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 3711–3718. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  18. Bonham, C.R., Parmee, I.C.: An investigation of exploration and exploitation within cluster oriented genetic algorithms (COGAs). In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1491–1497. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  19. Eshelman, L.J., Caruana, R.A., Schaffer, J.D.: Biases in the crossover landscape. In: Proceedings of the third international conference on Genetic algorithms, pp. 10–19. Morgan Kaufmann Publishers Inc., San Francisco (1989)

    Google Scholar 

  20. Eshelman, L., Schaffer, J.: Crossover’s niche. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 9–14. Morgan Kaufman, San Francisco (1993)

    Google Scholar 

  21. Eiben, A.E., Schippers, C.A.: On evolutionary exploration and exploitation. Fundamenta Informaticae 35, 35–50 (1998)

    MATH  Google Scholar 

  22. Jong, K.A.D., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence 5 (1992)

    Google Scholar 

  23. Tsutsui, S., Ghosh, A., Corne, D., Fujimoto, Y.: A real coded genetic algorithm with an explorer and an exploiter populations. In: Proceedings of the 7th International Conference on Genetic Algorithms, pp. 238–245. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  24. Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics. Springer, New York (2000)

    MATH  Google Scholar 

  25. Gustafson, S.M.: An Analysis of Diversity in Genetic Programming. PhD thesis, University of Nottingham (2004)

    Google Scholar 

  26. McMahon, A., Scott, D., Browne, W.: An autonomous explore/exploit strategy. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 103–108. ACM Press, New York (2005)

    Chapter  Google Scholar 

  27. Naudts, B., Schippers, A.: A motivated definition of exploitation and exploration. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, p. 800. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  28. Blickle, T., Thiele, L.: A mathematical analysis of tournament selection. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 9–16 (1995)

    Google Scholar 

  29. Downing, R.M.: Neutrality and gradualism: encouraging exploration and exploitation simultaneously with binary decision diagrams. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, pp. 615–622. IEEE Press, Los Alamitos (2006)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, New Zealand

    Huayang Xie & Mengjie Zhang

Authors
  1. Huayang Xie
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  2. Mengjie Zhang
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Editor information

Editors and Affiliations

  1. Clayton School of Information Technology, Monash University, 3800, Clayton, VIC, Australia

    Ann Nicholson

  2. School of Computer Science and Information Technology, RMIT University, 3001, Melbourne, VIC, Australia

    Xiaodong Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Xie, H., Zhang, M. (2009). Balancing Parent and Offspring Selection in Genetic Programming. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_46

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  • DOI: https://doi.org/10.1007/978-3-642-10439-8_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10438-1

  • Online ISBN: 978-3-642-10439-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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