Deep Learning Concepts for Evolutionary Art

  • Fazle Tanjil
  • Brian J. RossEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11453)


A deep convolutional neural network (CNN) trained on millions of images forms a very high-level abstract overview of an image. Our primary goal is to use this high-level content information a given target image to guide the automatic evolution of images using genetic programming. We investigate the use of a pre-trained deep CNN model as a fitness guide for evolution. Two different approaches are considered. Firstly, we developed a heuristic technique called Mean Minimum Matrix Strategy (MMMS) for determining the most suitable high-level CNN nodes to be used for fitness evaluation. This pre-evolution strategy determines the common high-level CNN nodes that show high activation values for a family of images that share an image feature of interest. Using MMMS, experiments show that GP can evolve procedural texture images that likewise have the same high-level feature. Secondly, we use the highest-level fully connected classifier layers of the deep CNN. Here, the user supplies a high-level classification label such as “peacock” or “banana”, and GP tries to evolve an image that maximizes the classification score for that target label. Experiments evolved images that often achieved high confidence scores for the supplied labels. However, the images themselves usually display some key aspect of the target required for CNN classification, rather than the entire subject matter expected by humans. We conclude that deep learning concepts show much potential as a tool for evolutionary art, and future results will improve as deep CNN models are better understood.


Deep convolutional neural network Genetic programming Evolutionary art 



This research was supported by NSERC Discovery Grant RGPIN-2016-03653.


  1. 1.
    Dawkins, R.: The Blind Watchmaker. Norton & Company, Inc. (1986)Google Scholar
  2. 2.
    Sims, K.: Artificial evolution for computer graphics. In: Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, vol. 25, no. 4, pp. 319–328, July 1991Google Scholar
  3. 3.
    Rooke, S.: Eons of genetically evolved algorithmic images. In: Bentley, P., Corne, D. (eds.) Creative Evolutionary Systems, pp. 339–365. Morgan Kaufmann, San Francisco (2002)CrossRefGoogle Scholar
  4. 4.
    Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press, London (1992)zbMATHGoogle Scholar
  5. 5.
    Bentley, P.: Creative Evolutionary Systems. Morgan Kaufmann, San Francisco (2002)CrossRefGoogle Scholar
  6. 6.
    Romero, J., Machado, P.: The Art of Artificial Evolution. Springer, Heidelberg (2008). Scholar
  7. 7.
    Graf, J., Banzhaf, W.: Interactive evolution of images. In: Proceedings 4th Evolutionary Programming, pp. 53–65. MIT Press (1995)Google Scholar
  8. 8.
    den Heijer, E., Eiben, A.E.: Comparing aesthetic measures for evolutionary art. In: Di Chio, C., et al. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010). Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar
  10. 10.
    Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)Google Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Proceedings 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  12. 12.
    Gatys, L., Ecker, A., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings Computer Vision and Pattern Recognition, pp. 2414–2423. IEEE, June 2016Google Scholar
  13. 13.
    Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)Google Scholar
  14. 14.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings Computer Vision and Pattern Recognition, pp. 886–893. IEEE (2005)Google Scholar
  15. 15.
    Bay, H., Tuytelaars, T., Van Gool, L.: Speeded up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  16. 16.
    Tanjil, F.: Deep learning concepts for evolutionary art. Master’s thesis, Department Computer Science, Brock U. (2018)Google Scholar
  17. 17.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  18. 18.
    Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming. Lulu Enterprises UK Ltd. (2008)Google Scholar
  19. 19.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  20. 20.
    Gooch, B., Gooch, A.: Non-photorealistic Rendering. A. K. Peters (2001)Google Scholar
  21. 21.
    Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 427–436, June 2015Google Scholar
  22. 22.
    Bontrager, P., Lin, W., Togelius, J., Risi, S.: Deep interactive evolution. In: Liapis, A., Romero Cardalda, J.J., Ekárt, A. (eds.) EvoMUSART 2018. LNCS, vol. 10783, pp. 267–282. Springer, Cham (2018). Scholar
  23. 23.
    Stanley, K.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8(2), 131–162 (2007)CrossRefGoogle Scholar
  24. 24.
    Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connection Sci. 6(2–3), 325–354 (1994)CrossRefGoogle Scholar
  25. 25.
    Agapitos, A., et al.: Deep evolution of image representations for handwritten digit recognition. In: Proceedings CEC 2015, Sendai, Japan, 25–28 May 2015, pp. 2452–2459. IEEE (2015)Google Scholar
  26. 26.
    Gircys, M.: Image evolution using 2D power spectra. Master’s thesis, Brock University, Department of Computer Science (2018)Google Scholar
  27. 27.
    Luke, S.: ECJ: a Java-based evolutionary computation research system. Accessed 16 Sept 2017
  28. 28.
    Chintala, S.: Pytorch documentation. Accessed 16 Sept 2017
  29. 29.
    Chintala, S.: PyTorch: tensors and dynamic neural networks in python with strong GPU acceleration. Accessed 16 Sept 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceBrock UniversitySt. CatharinesCanada

Personalised recommendations