Skip to main content
SpringerLink
Log in

Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation

  • Conference paper
Book cover Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

Included in the following conference series:

Abstract

Photomosaics are a new form of art in which smaller digital images (known as tiles) are used to construct larger images. Photomosaic generation not only creates interest in the digital arts area but has also attracted interest in the area of evolutionary computing. The photomosaic generation process may be viewed as an arrangement optimisation problem, for a given set of tiles and suitable target to be solved using evolutionary computing. In this paper we assess two methods used to represent photomosaics, genetic algorithms (GAs) and genetic programming (GP), in terms of their flexibility and efficiency. Our results show that although both approaches sometimes use the same computational effort, GP is capable of generating finer photomosaics in fewer generations. In conclusion, we found that the GP representation is richer than the GA representation and offers additional flexibility for future photomosaics generation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hinterding, R.: Representation, Mutation and Crossover Issues in Evolutionary Computation. In: Proceeding of Congress of 2000 Evolutionary Computation (CEC 2000), vol. 2, pp. 916–923. IEEE Service Center (2000)

    Google Scholar 

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

    MATH  Google Scholar 

  3. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1996)

    MATH  Google Scholar 

  4. Ciesielski, V., Berry, M., Trist, K., D’Souza, D.: Evolution of Animated Photomosaics. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 498–507. Springer, Heidelberg (2007)

    Google Scholar 

  5. Silvers, R., Hawley, M.: Photomosaic. Henry Holt and Company, Inc., New York (1997)

    Google Scholar 

  6. Finkelstein, A., Range, M.: Image Mosaic. In: Hersch, R.D., Andre, J., Brown, H. (eds.) RIDT 1998 and EPub 1998. LNCS, vol. 1375, pp. 11–22. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  7. Di Blasi, G., Gallo, G., Maria, P.: Smart Ideas for Photomosaic Rendering. In: Proceedings of Eurographics Italian Chapter Conference 2006, Eurographic Association, Catania, Italy (2006)

    Google Scholar 

  8. Kim, J., Pellacini, F.: Jigsaw Image Mosaics. ACM Transactions on Graphics (TOG) 21, 657–664 (2006)

    Google Scholar 

  9. Park, J.W.: Artistic depiction: Mosaic for Stacktable Objects. In: ACM SIGGRAPH 2004 Sketches SIGGRAPH 2004. ACM, New York (2004)

    Google Scholar 

  10. Wijesinghe, G., Mat Sah, S.B., Ciesielski, V.: Grid vs. Arbitrary Placement of Tiles for Generating Animated Photomosaics. In: Proceeding of Congress of 2008 Evolutionary Computation (CEC 2008). IEEE Service Center, Piscataway (2008)

    Google Scholar 

  11. Smith, R.E., Goldberg, D.E., Earickson, J.A.: SGA-C: A C-language Implementation of a Simple Genetic Algorithm (1991), http://citeseer.ist.psu.edu/341381.html

  12. Sinclair, M.C., Shami, S.H.: Evolving simple agents: Comparing genetic algorithm and genetic programming performance. In: IEE Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 421–426. IEEE Press, New York (1997)

    Google Scholar 

  13. Walker, M., Messom, C.H.: A Comparison of Genetic Programming and Genetic Algorithms for Auto-tuning Mobile Robot Motion Control. In: Proceedings of the First IEEE International Workshop on Electronic Design, Test and Applications (DELTA 2002), pp. 507–509. IEEE Press, New York (2002)

    Chapter  Google Scholar 

  14. Ebner, M.: On the search space of genetic programming and its relation to nature’s search space. In: Proceedings of the 1999 Congress on Evolutionary Computation, Washington, D.C, July 6-9, vol. 2, pp. 1357–1361. IEEE Press, Los Alamitos (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, GPO Box 2476V, Melbourne, Victoria, 3001, Australia

    Shahrul Badariah Mat Sah, Vic Ciesielski & Daryl D’Souza

  2. School of Creative Media, RMIT University, GPO Box 2476V, Melbourne, Victoria, 3001, Australia

    Marsha Berry

Authors
  1. Shahrul Badariah Mat Sah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vic Ciesielski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daryl D’Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marsha Berry
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

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

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mat Sah, S.B., Ciesielski, V., D’Souza, D., Berry, M. (2008). Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89694-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation