Abstract
We explore the use of Artificial Neural Network (ANN)-guided Genetic Programming (GP) to generate images that the guiding network classifies as belonging to a specific class. The experimental results demonstrate the ability of GP to perform such a task but also the inadequacy of most of the generated images, which can be considered false positives. Based on these findings and following an approach analogous to Generative Adversarial Networks (GANs), we propose an generative adversarial model where GP replaces the traditional GAN’s generator. The experimental results illustrate the advantages of this approach, highlighting the expressive power of GP, its capacity to perform online learning, thus adapting to a dynamic fitness landscape, and its ability to create novel imagery that fits the target classes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
TensorGP repository available at https://github.com/AwardOfSky/TensorGP.
- 2.
TGPGAN repository available at https://github.com/AwardOfSky/TensorGP_DCGAN.
- 3.
In our case, the Discriminator has a single output neuron, and it is trained to return 1 for genuine images and 0 for fake ones. In these circumstances, fitness is equal to the activation of the output neuron.
- 4.
Discriminator adapted from https://www.tensorflow.org/tutorials/generative/dcgan.
References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)
Baeta, F., Correia, J., Martins, T., Machado, P.: Speed benchmarking of genetic programming frameworks. In: GECCO, pp. 768–775. ACM (2021)
Baeta, F., Correia, J., Martins, T., Machado, P.: TensorGP - genetic programming engine in TensorFlow. In: Applications of Evolutionary Computation - 24th International Conference, Evo Applications 2021. pp, 763–778. Springer (2021)
Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connect. Sci. 6(2–3), 325–354 (1994)
Cayton, L.: Algorithms for manifold learning. University of California at San Diego Technical Report 12(1–17), 1 (2005)
Correia, J., Machado, P., Romero, J., Carballal, A.: Evolving figurative images using expression-based evolutionary art. In: Proceedings of the fourth International Conference on Computational Creativity (ICCC), pp. 24–31 (2013)
Correia, J., Machado, P., Romero, J., Martins, P., Cardoso, F.A.: Breaking the mould an evolutionary quest for innovation through style change. In: Computational Creativity, pp. 353–398. Springer (2019)
Correia, J., Martins, T., Machado, P.: Evolutionary data augmentation in deep face detection. In: GECCO (Companion), pp. 163–164. ACM (2019)
Correia, J., Martins, T., Martins, P., Machado, P.: X-faces: the eXploit is out there. In: ICCC, pp. 164–171. Sony CSL Paris, France (2016)
Costa, V., Lourenço, N., Correia, J., Machado, P.: COEGAN: evaluating the coevolution effect in generative adversarial networks. In: GECCO, pp. 374–382. ACM (2019)
Costa, V., Lourenço, N., Correia, J., Machado, P.: Demonstrating the evolution of GANs through t-SNE. In: EvoApplications. Lecture Notes in Computer Science, vol. 12694, pp. 618–633. Springer (2021)
Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141–142 (2012)
Ekern, E.G., Gambäck, B.: Interactive, efficient and creative image generation using compositional pattern-producing networks. In: EvoMUSART. Lecture Notes in Computer Science, vol. 12693, pp. 131–146. Springer (2021)
Goodfellow, I.: NIPS 2016 tutorial: generative adversarial networks (2017). https://doi.org/10.48550/ARXIV.1701.00160
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)
Greenfield, G.R.: Evolving expressions and art by choice. Leonardo 33(2), 93–99 (2000)
Greenfield, G.R.: Tilings of sequences of co-evolved images. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) Applications of Evolutionary Computing, EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Coimbra, Portugal, April 5–7, 2004, Proceedings. Lecture Notes in Computer Science, vol. 3005, pp. 427–436. Springer (2004). https://doi.org/10.1007/978-3-540-24653-4_44
Ha, D.: The frog of CIFAR 10. blog.otoro.net (2016). https://blog.otoro.net/2016/04/06/the-frog-of-cifar-10/
Ha, D.: Generating large images from latent vectors. blog.otoro.net (2016). https://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/
Hemberg, E., Toutouh, J., Al-Dujaili, A., Schmiedlechner, T., O’Reilly, U.M.: Spatial coevolution for generative adversarial network training. ACM Trans. Evol. Learn. Optim. 1(2) (2021). https://doi.org/10.1145/3458845
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Lewis, M.: Evolutionary visual art and design. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art And Music, pp. 3–37. Springer (2008)
Machado, P., Cardoso, A.: NEvAr - the assessment of an evolutionary art tool. In: Wiggins, G. (ed.) AISB’00 Symposium on Creative and Cultural Aspects and Applications of AI and Cognitive Science. Birmingham, UK (2000)
Machado, P., Cardoso, A.: All the truth about NEvAr. Appl. Intell. 16(2), 101–118 (2002)
Machado, P., Correia, J., Romero, J.: Expression-based evolution of faces. In: Evolutionary and Biologically Inspired Music, Sound, Art and Design - First International Conference, EvoMUSART 2012, Málaga, Spain, April 11–13, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7247, pp. 187–198. Springer (2012). https://doi.org/10.1007/978-3-642-29142-5_17
Machado, P., Correia, J., Romero, J.: Improving face detection. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) Genetic Programming - 15th European Conference, EuroGP 2012, Málaga, Spain, April 11–13, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7244, pp. 73–84. Springer (2012). https://doi.org/10.1007/978-3-642-29139-5_7
Machado, P., Romero, J., Greenfield, G.: Artificial Intelligence and the Arts - Computational Creativity, Artistic Behavior, and Tools for Creatives. Springer (2021). https://doi.org/10.1007/978-3-030-59475-6
Machado, P., Romero, J., Manaris, B.: Experiments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-72877-1_18
Machado, P., Vinhas, A., Correia, J., Ekárt, A.: Evolving ambiguous images. In: Yang, Q., Wooldridge, M. (eds.) Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25–31, 2015, pp. 2473–2479. AAAI Press (2015). http://ijcai.org/papers15/Abstracts/IJCAI15-350.html
Martins, T., Correia, J., Costa, E., Machado, P.: Evolving stencils for typefaces: combining machine learning, user’s preferences and novelty. Complexity 2019 (2019). https://doi.org/10.1155/2019/3509263
Metz, L., Gulrajani, I.: Compositional pattern producing GAN. In: NeurIPS Workshops, vol. 1 (2017)
Nguyen, A.M., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7–12, 2015, pp. 427–436. IEEE Computer Society (2015)
Romero, J., Machado, P. (eds.): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Natural Computing Series. Springer (2008)
Rooke, S.: Eons of genetically evolved algorithmic images. In: Bentley, P.J., Corne, D.W. (eds.) Creative Evolutionary Systems, pp. 339–365. Morgan Kaufmann (2002)
Secretan, J., Beato, N.: Picbreeder: evolving pictures collaboratively online. In: Czerwinski, M., Lund, A.M., Tan, D.S. (eds.) Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI 2008, 2008, Florence, Italy, April 5–10, 2008, pp. 1759–1768. ACM (2008). https://doi.org/10.1145/1357054.1357328
Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: a case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)
Sims, K.: Artificial evolution for computer graphics. In: SIGGRAPH ’91: Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, pp. 319–328. ACM, New York (1991). https://doi.org/10.1145/122718.122752
Spector, L., Alpern, A.: Criticism, culture and the automatic generation of artworks. In: Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 3–8. AAAI Press, Menlo Park (1994)
Staats, K., Pantridge, E., Cavaglia, M., Milovanov, I., Aniyan, A.: TensorFlow enabled genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 1872–1879. ACM (2017)
Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program Evolvable Mach. 8(2), 131–162 (2007)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
Todd, S., Latham, W.: Evolutionary Art and Computers. Academic (1992)
Unemi, T.: SBART 2.4: breeding 2D CG images and movies and creating a type of collage. In: Knowledge-Based Intelligent Information Engineering Systems, 1999. Third International Conference, pp. 288–291. IEEE (1999)
Wang, C., Xu, C., Yao, X., Tao, D.: Evolutionary generative adversarial networks. IEEE Trans. Evol. Comput. 23(6), 921–934 (2019)
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (2017). arXiv preprint arXiv:1708.07747
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgements
This work is funded by national funds through the FCT—Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit—UIDB/00326/2020 or project code UIDP/00326/2020 and by European Social Fund, through the Regional Operational Program Centro 2020, under the FCT grant SFRH/BD/08254/2021.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Machado, P., Baeta, F., Martins, T., Correia, J. (2023). GP-Based Generative Adversarial Models. In: Trujillo, L., Winkler, S.M., Silva, S., Banzhaf, W. (eds) Genetic Programming Theory and Practice XIX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-19-8460-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-19-8460-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8459-4
Online ISBN: 978-981-19-8460-0
eBook Packages: Computer ScienceComputer Science (R0)