Skip to main content
SpringerLink
Log in

Hands-on Artificial Evolution Through Brain Programming

  • Chapter
  • First Online:
Book cover Genetic Programming Theory and Practice XVII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

This paper is about the evolution of a bio-inspired methodologyhttp://evovision.cicese.mx that mimics the cortical visual pathways. The methodology has been extensively tested on problems with different levels of complexity with outstanding results. After a review of the main works, the problem of classification of digitized art is introduced. An image database of five classes downloaded from the Kaggle web site is used as a benchmark for evolutionary learning. A comparison with convolutional neural network from scratch and the well-known AlexNet is provided to illustrate the quality of the proposal in comparison with the state-of-the-art.

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    Nowadays, to claim that any computational method (artificial intelligence) is capable of solving similar visual problems needs to be taken carefully since the programs need to be explainable from the artistic viewpoint.

  2. 2.

    Note that Koza classify neural networks as one of the existing methods that do not seek solutions in the form of computer programs.

References

  1. Agarwal, S., Karnick, H., Pant, N., Patel, U.: Genre and style based painting classification. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 588–594. IEEE (2015)

    Google Scholar 

  2. Arora, R. S., Elgammal, A.: Towards automated classification of fine-art painting style: A comparative study. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR-2012), pp. 3541–3544. IEEE (2012)

    Google Scholar 

  3. Art Images: Drawing/Engraving/Iconography/Painting/Sculpture, https://www.kaggle.com/thedownhill/art-images-drawings-painting-sculpture-engraving

  4. Bar, Y., Levy, N., Wolf, L.: Classification of artistic styles using binarized features derived from a deep neural network. In: European Conference on Computer Vision, pp. 71–84. Springer, Cham (2014)

    Google Scholar 

  5. Clemente, E., Olague, G., Dozal, L., Mancilla, M.: Object Recognition with an Optimized Ventral Stream Model using Genetic Programming. European Conference on the Applications of Evolutionary Computation, pp. 315–325. EvoApplications (2012)

    Google Scholar 

  6. Clemente, E., Chavez de la O, F., Fernández, F., Olague, G.: Self-adjusting Focus of Attention in Combination with a Genetic Fuzzy System for Improving a Laser Environment Control Device System. Applied Soft Computing 32, 250–265 (2015)

    Article  Google Scholar 

  7. Condorovici, R. G., Florea, C., Vrânceanu, R., Vertan, C.: Perceptually-inspired artistic genre identification system in digitized painting collections. In: Scandinavian Conference on Image Analysis, pp. 687–696. Springer (2013)

    Google Scholar 

  8. Dozal, L., Olague, G., Clemente, E., Sánchez, M.: Evolving Visual Attention Programs through EVO Features. European Conference on the Applications of Evolutionary Computation, pp. 326–335. EvoApplications (2012)

    Google Scholar 

  9. Dozal, L., Olague, G., Clemente, E., Hernández, D.E.: Brain Programming for the Evolution of an Artificial Dorsal Stream. Cognitive Computation 6(3) 528–557 (2014)

    Article  Google Scholar 

  10. Florea, C., Toca, C., Gieseke, F.: Artistic movement recognition by boosted fusion of color structure and topographic description. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 569–577. IEEE (2017)

    Google Scholar 

  11. Fukushima, K.: Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics 36(4), 193–202 (1980)

    Article  Google Scholar 

  12. Hernández, D.E., Clemente, E., Olague, G., Briseño, J.L.: Evolutionary Multi-objective Visual Cortex for Object Classification in Natural Images. Journal of Computational Science 17(part 1), 216–233 (2016)

    Google Scholar 

  13. Hernández, D.E., Olague, G., Hernández, B., Clemente, E.: CUDA-based Parallelization of a Bio-inspired Model for Fast Object Classification. Neural Computing and Applications 30(10), 3007–3018 (2018)

    Article  Google Scholar 

  14. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, 211 pages—first appeared in 1975 (1992)

    Google Scholar 

  15. Kowaliw, T., McCormack, J., Dorin, A.: Evolutionary automated recognition and characterization of an individual’s artistic style. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

    Google Scholar 

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

    Google Scholar 

  17. Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images. April 2009.

    Google Scholar 

  18. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, NIPS (2012)

    Google Scholar 

  19. Le, Q.V., Ranzato, M.A., Monga, R., Devin, M., Chen, K., Corrado, G.S., Dean, J., Ng, A.Y.: Building High-level Features using Large Sacle Unsupervised Learning. International Conference on Machine Learning, pp. 507–514 ICML (2012)

    Google Scholar 

  20. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based Learning Applied to Document Recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  21. Lyu, S., Rockmore, D., Farid, H.: A digital technique for art authentication. Proceedings of the National Academy of Sciences, 101(49), 17006–17010 (2004)

    Article  Google Scholar 

  22. Olague, G.: Evolutionary Computer Vision: The First Footprints. Springer, Heidelberg (2016)

    Book  Google Scholar 

  23. Olague, G., Hernández, D.E., Clemente, E., Chan-Ley, M.: Evolving Head Tracking Routines with Brain Programming. IEEE Access 6, 26254–26270 (2018)

    Article  Google Scholar 

  24. Olague, G., Hernández, D.E., Llamas, P., Clemente, E., Briseño, J.L.: Brain Programming as a New Strategy to Create Visual Routines for Object Tracking. Multimedia Tools and Applications 78(5), 5881–5918, (2018)

    Article  Google Scholar 

  25. Olague, G., Clemente, E., Hernández, D.E., Barrera, A., Chan-Ley, M., Bakshi, S.: Artificial Visual Cortex and Random Search for Object Categorization. IEEE Access (2019)

    Google Scholar 

  26. Olague, G.: Automated Photogrammetric Network Design using Genetic Algorithms. Photogrammetric Engineering & Remote Sensing 68(5), 423–431 (2002) Paper awarded the “2003 First Honorable Mention to the Talbert Abrams Award”, by ASPRS.

    Google Scholar 

  27. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van den Driessche, G., Schrittwieser J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman. S., Grewe, D., Nham, J., Kalchbrenner, N., Sustkever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature 529, 484–489 (2016)

    Article  Google Scholar 

  28. Zujovic, J.,Gandy, L., Friedman, S., Pardo, B., Pappas, T.: Classifying Paintings by Artistic Genre: An Analysis of Features & Classifiers. In: 2009 IEEE International Workshop on Multimedia Signal Processing, pp. 1–5. IEEE (2009)

    Google Scholar 

Download references

Acknowledgements

This research was partially funded by CICESE through the project 634-128, “Programación Cerebral Aplicada al Estudio del Pensamiento y la Visión”. The second author graciously acknowledges the scholarship paid by the National Council for Science and Technology of Mexico (CONACyT) under grant 25267-340078.

Author information

Authors and Affiliations

  1. CICESE, Ensenada, BC, Mexico

    Gustavo Olague

  2. EvoVisión Laboratory, Ensenada, BC, Mexico

    Mariana Chan-Ley

Authors
  1. Gustavo Olague
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariana Chan-Ley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gustavo Olague .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. BEACON Center, Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. Department of Computer Science and Engineering, Michigan State University, Okemos, MI, USA

    Leigh Sheneman

  4. Depto Ingenieria en Electronic Electrica Tecnológico Nacional de México/ IT, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  5. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Olague, G., Chan-Ley, M. (2020). Hands-on Artificial Evolution Through Brain Programming. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-39958-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39957-3

  • Online ISBN: 978-3-030-39958-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation