Photogrowth: Ant Painting

Photogrowth is an evolutionary approach to the creation of non-photorealistic renderings of images. Inspired by ant colony approaches, the painting algorithm, simulates the behaviour of species of artificial ants using their trails to produce a rendering of an original input image. One of the novel characteristics of this algorithm is the adoption of scalable vector graphics, which contrasts with the pixel based approaches used in most ant painting algorithms, and enables the creation of resolution independent images. The rendering algorithm represents the trail of each ant through a continuous line of varying width, contributing to the expressiveness of the NPRs.
In spite of the potential of the algorithm, the number of parameters controlling the behavior of the ants and their interdependencies soon revealed to be too large to allow their fine-tuning by hand. The results of these attempts, revealed that only a small and narrow subset of the creative possibilities allowed by the algorithm was being explored.
To tackle this problem, we presented and used an interactive Genetic Algorithm to evolve the parameters, allowing the users to guide the algorithm according to their preferences and avoiding the need to understand the intricacies of the algorithm. Thus, instead of being forced to perform low-level changes, the users become breeders of species of ants that produce results that they find valuable. The experimental results highlight the range of imagery that can be evolved by the system showing its potential for the production of large-format artworks.
The latest incarnation of Photogrowth constitutes a further step in the automation of the space exploration process and departure from low-level modification and assessment. The users become designers of fitness functions, which are used to guide evolution, ideally leading to results that are consistent with the user intentions. To this end, while the ants paint, statistics describing their behavior are gathered. Once each painting is completed image features are calculated. These behavioral and image features are the basis for the creation of the fitness functions.

Figure 1

Painting nude #7




  • The paper “An Interface for Fitness Function Design” by Penousal Machado, Tiago Martins, Hugo Amaro and Pedro H. Abreu has won the best paper award at EvoMUSART 2014.


Presence in exhibitions


  • Living Machines exhibition, London Science Museum, London, UK (August 1, 2013).
  • BRIDGES 2012 – Mathematical Art Galleries, Towson, MA, USA, 2012.


Related projects





  • T. Barrass, A. Eldridge, G. Greenfield, C. Jacob, P. Machado, N. Monmarché, Y. Semet, F. Durand, U. O’Reilly, D. Shiffman, and P. Urbano, “Gallery Artists,” Leonardo, vol. 47, iss. 1, pp. 8-16, 2014.

In Proceedings

  • P. Machado, T. Martins, H. Amaro, and P. H. Abreu, “An Interface for Fitness Function Design,” in Evolutionary and Biologically Inspired Music, Sound, Art and Design – Third International Conference, EvoMUSART 2014, Granada, Spain, April 23-25, 2014. Proceedings, 2014.

  • P. Machado and H. Amaro, “Fitness Functions for Ant Colony Paintings,” in Proceedings of the fourth International Conference on Computational Creativity (ICCC), 2013, pp. 32-39.

  • P. Machado and L. Pereira, “Photogrowth: non-photorealistic renderings through ant paintings,” in Genetic and Evolutionary Computation Conference, GECCO ’12, Philadelphia, PA, USA, July 7-11, 2012, 2012, pp. 233-240.