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Parallel Linear Genetic Programming

  • Carlton Downey
  • Mengjie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consist of n lists of instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP.

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References

  1. 1.
    Andre, D., Koza, J.R.: A parallel implementation of genetic programming that achieves super-linear performance. Information Sciences 106(3-4), 201–218 (1998)CrossRefGoogle Scholar
  2. 2.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction on the Automatic Evolution of computer programs and its Applications. Morgan Kaufmann Publishers, Dpunkt-Verlag, San Francisco, Heidelburg (1998)Google Scholar
  3. 3.
    Brameier, M., Banzhaf, W.: Linear Genetic Programming. Genetic and Evolutionary Computation, vol. XVI. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  4. 4.
    Fogelberg, C., Zhang, M.: Linear genetic programming for multi-class object classification. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 369–379. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, MIT Press, Ann Arbor, Cambridge (1975)zbMATHGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  7. 7.
    Krawiec, K., Bhanu, B.: Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Transactions on Evolutionary Computation 11(5), 635–650 (2007)CrossRefGoogle Scholar
  8. 8.
    Olague Caballero, G., Romero, E., Trujillo, L., Bhanu, B.: Multiclass object recognition based on texture linear genetic programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 291–300. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Olaguea, G., Cagnoni, S., Lutton, E. (eds.): special issue on evolutionary computer vision and image understanding. Pattern Recognition Letters 27(11) (2006)Google Scholar
  10. 10.
    Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  11. 11.
    Zhang, M., Ciesielski, V.B., Andreae, P.: A domain-independent window approach to multiclass object detection using genetic programming. EURASIP Journal on Applied Signal Processing 2003(8), 841–859 (2003); special Issue on Genetic and Evolutionary Computation for Signal Processing and Image AnalysisCrossRefzbMATHGoogle Scholar
  12. 12.
    Zhang, M., Gao, X., Lou, W.: A new crossover operator in genetic programming for object classification. IEEE Transactions on Systems, Man and Cybernetics, Part B 37(5), 1332–1343 (2007)CrossRefGoogle Scholar
  13. 13.
    Zhang, M., Smart, W.: Using gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recognition Letters 27(11), 1266–1274 (2006); evolutionary Computer Vision and Image UnderstandingCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlton Downey
    • 1
  • Mengjie Zhang
    • 1
  1. 1.Victoria University of WellingtonWellingtonNew Zealand

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