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Outline

Dynamic Subset Selection Based on a Fitness Case Topology

2004, Evolutionary Computation

https://doi.org/10.1162/106365604773955157

Abstract

A large training set of fitness cases can critically slow down genetic programming, if no appropriate subset selection method is applied. Such a method allows an individual to be evaluated on a smaller subset of fitness cases. In this paper we suggest a new subset selection method that takes the problem structure into account, while being problem independent at the same time. In order to achieve this, information about the problem structure is acquired during evolutionary search by creating a topology (relationship) on the set of fitness cases. The topology is induced by individuals of the evolving population. This is done by increasing the strength of the relation between two fitness cases, if an individual of the population is able to solve both of them. Our new topology—based subset selection method chooses a subset, such that fitness cases in this subset are as distantly related as is possible with respect to the induced topology. We compare topology—based selection of fitness c...

References (30)

  1. = input[0] * M_PI;
  2. = 5 + temp[3];
  3. = tan(temp[1]);
  4. = tan(temp[1]);
  5. = M_E -20;
  6. = input[1] * M_PI;
  7. Altenberg, L. (1994). The evolution of evolvability in genetic programming. In Kin- near, Jr., K. E., editor, Advances in Genetic Programming, pages 47-74. MIT Press, Cambridge, MA.
  8. Banzhaf, W., Nordin, P., Keller, R. E., and Francone, F. D. (1998). Genetic Programming -An Introduction. Morgan Kaufmann, San Francisco, CA.
  9. Blake, C. L. and Merz, C. J. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/∼mlearn/MLRepository.html.
  10. Gathercole, C. (1998). An Investigation of Supervised Learning in Genetic Programming. PhD thesis, University of Edinburgh.
  11. Gathercole, C. and Ross, P. (1994). Dynamic training subset selection for supervised learning in genetic programming. In Davidor, Y., Schwefel, H.-P., and Männer, R., editors, Parallel Problem Solving from Nature III, volume 866 of LNCS, pages 312-321, Berlin. Springer-Verlag.
  12. Gathercole, C. and Ross, P. (1997). Small populations over many generations can beat large populations over few generations in genetic programming. In Koza, J. R., Deb, K., Dorigo, M., Fogel, D. B., Garzon, M., Iba, H., and Riolo, R. L., editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 111- 118, Stanford University, CA, USA. Morgan Kaufmann.
  13. Goldberg, D. E. and Richardson, J. (1987). Genetic algorithms with sharing for multi- modal function optimization. In Grefenstette, J., editor, Genetic Algorithms and their Applications (ICGA'87), pages 41-49. Lawrence Erlbaum Associates.
  14. Hogg, T. (1996). Refining the phase transition in combinatorial search. Artificial Intelli- gence, 81(1-2):127-154.
  15. Holland, J. (1975). Adaption in natural and artificial systems. MIT Press, Cambridge, MA.
  16. Keijzer, M. (2001). Scientific Discovery using Genetic Programming. PhD thesis, Technical University of Denmark.
  17. Koza, J. R. (1992a). A genetic approach to the truck backer upper problem and the inter-twined spiral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, volume IV, pages 310-318. IEEE Press.
  18. Koza, J. R. (1992b). Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge, MA.
  19. Lang, K. J. and Witbrock, M. J. (1989). Learning to tell two spirals apart. In Proceedings of the 1988 Connectionist Models Summer School, pages 52-59, San Mateo, CA. Morgan Kaufmann.
  20. Lasarczyk, C. W. G. (2002). Trainingsmengenselektion auf der Grundlage einer Fitnesscase-Topologie. Diploma thesis, University of Dortmund.
  21. Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics -Doklady, 10(8):707-710. Original in Russian in Doklady Akademii Nauk SSSR, 163, 4, 845-848, 1965.
  22. Miglino, O. and Walker, R. (2002). Genetic redundancy in evolving populations of simulated robots. Artificial Life, 8(3):265-277.
  23. Evolutionary Computation Volume 12, Number 2
  24. Nordin, P. and Banzhaf, W. (1997). An on-line method to evolve behavior and to con- trol a miniature robot in real time with genetic programming. Adaptive Behaviour, 5(2):107-140.
  25. Schaffer, J. D., Caruana, R. A., Eshelman, L. J., and Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function opti- misation. In Schaffer, J. D., editor, Proceedings of the 3rd International Conference on Genetic Algorithms, pages 51-60, San Mateo, California. George Mason University, Morgan Kaufmann Publishers.
  26. Voudouris, C. (1998). Guided local search -an illustrative example in function optimi- sation. BT Technology Journal, 16(3):46-50.
  27. Voudouris, C. and Tsang, E. (1996). Partial constraint satisfaction problems and guided local search. In Wallace, M., editor, Proceedings of the Second International Conference on the Practical Application of Constraint Technology (PACT'96), pages 337-356. The Practical Application Company Ltd.
  28. Wagner, P. and Altenberg, L. (1996). Complex adaptations and the evolution of evolv- ability. Evolution, 50(3):967-976.
  29. Walsh, T. (1999). Search in a small world. In Thomas, D., editor, Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI'99), volume 2, pages 1172-1177, San Francisco, CA, USA. Morgan Kaufmann Publishers.
  30. Zhang, B.-T. and Cho, D.-Y. (1998). Genetic programming with active data selection. In Newton, C., editor, Proceedings of the Second Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'98), volume 1.