Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis

  • Contributed Article
  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Redundant representations may help evolutionary search to escape from local optima by means of neutral networks, i.e., the sets of genotypes that represent the same phenotype and are connected by single mutation [13].

  2. Although an algorithm for detecting semantic introns was presented in [7], it increases the number of program evaluations greatly and would not be practical.

References

  1. J. Ando, T. Nagao, Fast tree-structural image processing using GPU, in Proceedings of IWAIT-2007, Bangkok, Thailand, 2007, pp. 423–428

  2. S. Aoki, T. Nagao, Automatic construction of tree-structural image transformations using genetic programming, in Proceedings of ICAIP-99, Venezia, Italy, 1999, pp. 136–141

  3. W. Banzhaf, A. Leier, Evolution on neutral networks in genetic programming, in Genetic Programming Theory and Practice III, ed. by T. Yu, R. Riolo, B. Worzel (Springer, Berlin, 2006)

    Google Scholar 

  4. W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. (Morgan Kaufmann, Massachusetts, 1997)

    Google Scholar 

  5. M. Brameier, W. Banzhaf, A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 5(1), 17–26 (2001)

    Article  Google Scholar 

  6. M. Brameier, W. Banzhaf, Explicit control of diversity and effective variation distance in linear genetic programming, in EuroGP 2002 , ed. by J.A. Foster et al. LNCS, vol. 2278 (Springer, Berlin, 2002), pp. 37–49

  7. M. Brameier, W. Banzhaf, Linear Genetic Programming. (Springer, Berlin, 2007)

    MATH  Google Scholar 

  8. E. Burke, S. Gustafon, G. Kendall, N. Krasnogor, Advance population diversity measures in genetic programming, in Parallel Problem Solving from Nature—PPSN VII , ed. by J. Merelo-Guervos et al. LNCS, vol. 2439 (Springer, Berlin, 2002), pp. 341–350

  9. E. Burke, S. Gustafon, G. Kendall, N. Krasnogor, Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004)

    Article  Google Scholar 

  10. K.A. De Jong, Evolutionary Computation: A Unified Approach (MIT Press, Cambridge, 2006)

    MATH  Google Scholar 

  11. B. Draper, U. Ahlrichs, D. Paulus, Adaptive object recognition across domains: a demonstration, in Proceedings of International Conference on Vision Systems, Vancouver, BC, 2001, pp. 256–267

  12. B. Draper, J. Bins, K. Baek, ADORE: adaptive object recognition. Videre 4(1), 86–99 (2000)

    Google Scholar 

  13. M. Ebner, M. Shackleton, R. Shipman, How neutral networks influence evolvability. Complexity 7(2), 19–33 (2001)

    Article  MathSciNet  Google Scholar 

  14. R.C. Gonzalez, R.E. Woods, Digital Image Processing (Addison-Wesley, Reading, 1992)

    Google Scholar 

  15. G.K. Kanji, 100 Statistical Tests, 3rd edn. (Sage, Beverly Hills, 2006)

    Google Scholar 

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

    MATH  Google Scholar 

  17. K. Krawiec, Generative learning of visual concepts using multiobjective genetic programming. Pattern Recognit. Lett. 28(16), 2385–2400 (2007)

    Article  Google Scholar 

  18. K. Krawiec, B. Bhanu, Visual learning by coevolutionary feature synthesis. IEEE Trans. Syst. Man Cybern. Part B 35(3), 409–425 (2005)

    Article  Google Scholar 

  19. K. Krawiec, B. Bhanu, Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Trans. Evol. Comput. 11(5), 635–650 (2007)

    Article  Google Scholar 

  20. W.B. Langdon, W. Banzhaf, Repeated sequences in linear genetic programming genomes. Complex Syst. 15(4), 285–306 (2005)

    MATH  MathSciNet  Google Scholar 

  21. J.R. Levenick, Inserting introns improves genetic algorithm success rate: taking a cue from biology, in Proceedings of International Conference on Genetic Algorithm (ICGA-91), ed. by R.K. Belew, L.B. Booker (Morgan Kaufmann, Massachusetts, 1991), pp. 123–127

  22. I. Levner, V. Bulitko, L. Li, G. Lee, R. Greiner, Towards automated creation of image interpretation systems, in Proceedings of 16th Australian Joint Conference on Artificial Intelligence (Perth, Australia, 2003), pp. 653–665

  23. R.W. Morrison, K.A. De Jong, Measurement of population diversity, in Artificial Evolution. LNCS, vol. 2310 (Springer, Berlin, 2002), pp. 1047–1074

  24. T. Nagao, S. Masunaga, Automatic construction of image transformation processes using genetic algorithm, in Proceedings of ICIP-96, vol. 3 (Lausanne, Switzerland, 1996), pp. 731–734

  25. J. Niehaus, C. Igel, W. Banzhaf, Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database. Evol. Comput. 15(2), 199–221 (2007)

    Article  Google Scholar 

  26. P. Nordin, W. Banzhaf, F.D. Francone, Introns in nature and in simulated structure evolution, in Proceedings of BCEC-97, ed. by D. Lundh, B. Olsson, A. Narayanan (World Scientific, Singapore, 1997), pp. 22–35

  27. P. Nordin, F.D. Francone, W. Banzhaf, Explicitly defined introns and destructive crossover, in Advance in Genetic Programming II, ed. by P.J. Angeline, K. Kinear (MIT Press, Cambridge, 1996), pp. 111–134

    Google Scholar 

  28. P. Poli, W.B. Langdon, N.F. McPhee, A Filed Guide to Genetic Programming (2008). Published via http://lulu.com. http://www.gp-field-guide.org.uk

  29. M. Roberts, E. Claridge, Co-operative coevolution of image feature construction and object detection, in Parallel Problem Solving from Nature—PPSN VIII. LNCS, vol. 3242 (Springer, Birmingham, 2004), pp. 899–908

  30. M. Roberts, E. Claridge, A multi-stage approach to cooperatively coevolving feature construction and object detection, in Application of Evolutionary Computing, ed. by F. Rothlauf et al. LNCS, vol. 3449 (Springer, Berlin, 2005), pp. 396–406

    Chapter  Google Scholar 

  31. S. Shirakawa, T. Nagao, Genetic image network (GIN): automatically construction of image processing, in Proceedings of IWAIT-2007, Bangkok, Thailand, 2007, pp. 643–648

  32. A. Teller, M. Veloso, A controlled experiment: evolution for learning difficult image classification, in Proceedings of Seventh Portuguese Conf. Artificial Intelligence. LNCS, vol. 990, Berlin, Germany, 1995, pp. 165–185

  33. A. Teller, M. Veloso, PADO: a new learning architecture for object recognition, in Symbolic Visual Learning ed. by K. Ikeuchi, M. Veloso (Oxford University Press, Oxford, 1997), pp. 77–112

    Google Scholar 

  34. S. Theodoridis, K. Koutroumbas, Pattern Recognition, 3rd edn. (Academic Press, London, 2006)

    MATH  Google Scholar 

  35. L. Trujillo, G. Olague, Automated design of image operators that detect interest points. Evol. Comput. 16(4), 483–507 (2008)

    Article  Google Scholar 

  36. R.K. Urasem, Diversity-guided evolutionary algorithms, in Parallel Problem Solving from Nature—PPSN VII, ed. by J. Merelo-Guervos et al. LNCS, vol. 2439 (Springer, Berlin, 2002), pp. 462–471

    Chapter  Google Scholar 

  37. J.R. Woodward, R. Bai, Canonical representation genetic programming, in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, Shanghai, China, pp. 585–592

  38. S.Y. Yuen, C.K. Chow, A non-revisiting genetic algorithm, in Proceedings of IEEE Congress on Evolutionary Computation (CEC-2007), Singapore, 2007, pp. 4583–4590

  39. M. Zhang, V. Ciesielski, P. Andreae, A domain-independent window approach to multiclass object detection using genetic programming. EURASIP J. Appl. Signal Process. 8, 841–859 (2003)

    Google Scholar 

Download references

Acknowledgements

This work was supported by a research grant from the Hori Information Science Promotion Foundation. We thank the anonymous reviewers for their useful comments that help us improve the paper.

Author information

Author notes

  1. Ukrit Watchareeruetai

    Present address: International College, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang District, Bangkok, 10520, Thailand

Authors and Affiliations

  1. Department of Media Science, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Ukrit Watchareeruetai, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo & Noboru Ohnishi

Authors
  1. Ukrit Watchareeruetai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshinori Takeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tetsuya Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroaki Kudo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noboru Ohnishi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ukrit Watchareeruetai.

Additional information

Portions of this work are based on “Transformation of redundant representations of linear genetic programming into canonical forms for efficient extraction of image features”, by U. Watchareeruetai et al. which appeared in Proceedings of 2008 IEEE Congress on Evolutionary Computation. ©2008 IEEE.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Watchareeruetai, U., Takeuchi, Y., Matsumoto, T. et al. Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis. Genet Program Evolvable Mach 12, 49–77 (2011). https://doi.org/10.1007/s10710-010-9118-x

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-010-9118-x

Keywords

Search

Navigation