Pherographia in Leonardo Journal

My paper “Pherographia: Drawing by Ants”, published  last April at Leonard Journal, vol. 43 (2), is now available at my webpage, here. The paper was written two years ago, when I was still reflecting on how to integrate a swarm intelligence system that Ramos and Almeida [1] devised after the seminal model by Chialvo and Milona  [2] on my photographic body-of-work (I later contributed to speed up the process, by adding a reproduction scheme to the model [3], and, somehow, “our” KANTS algorithm [4, 5] was also inspired by Ramos and Almeida’s paper). By then I was mainly interested in the similarities between pherographia –  the term coined to describe the process – and photographia (independently of being a system that may detect the edges of photos, as you may notice if you read the paper, which is not about the model itself, but about its potential as an artistic tool, about artificial art, and the metaphors that may inspired creative artwork), based on my experience as a photographer and as a black-and-white darkroom user. In addition, I tried to contextualize my experiments and works in the artificial art trend (I think I had better results on later essays on the theme of art and science, that you may find in my webpage). This is the abstract of Leonardo’s paper:

This paper addresses the hypothetical relationship of Photography and the so-called pheromone maps created by an Artificial Life system that simulates an ant colony and evolves it on monochromatic images. Pheromone — the substance used by ants to communicate via the environment — is also simulated, and, from the communication and interaction of the swarm with the environment (image), results a kind of drawing made with the artificial pheromone. Since ants are able to detect the edges of the image, the outcome is a sketch that resembles the original picture, like the old camera obscura’s drawings. The term Pherographia — meaning drawing with pheromone — arises from the analogy with camera obscura and Photography but the text goes beyond the metaphorical links between Pherographia and Photographia and explores the observable traits shared by the photographic process and the swarm’s pheromone maps. The theme is discussed within the emergent Artificial Art research field and recent theoretical advances that link Swarm Intelligence and cognitive sciences are also addressed.

In the last two years I have created some artwork based on the swarm model (namelly, Timor Mortis Conturbat Me and The Horse and the Ants). This artwork has been exhibited to an heterogeneous audience. I expect to show soon a report on these efforts.

[1] V. Ramos, F. Almeida, “Artificial Ant Colonies in Digital Image Habitats A Mass Behaviour Effect Study on Pattern Recognition”, Marco Dorigo, Martin Middendoff and Thomas Suetzle (Eds.), Proceedings 2nd International Workshop on Ant Algorithms, pp. 113-116 (2000).

[2] D. Chialvo, M. Milonas, “How Swarms Build Cognitive Maps”, Luc Steels (Ed.), The Biology and Technology of Intelligent Autonomous Agents, No. 144, NATO ASI Series, pp. 439-450 (1995).

[3] C. M. Fernandes, V. Ramos, A. C. Rosa, “Self-Regulated Artificial Ant Colonies on Digital Image Habitats”, International Journal of Lateral Computing Vol. 2, No. 1, pp. 1-8 (2005).

[4] A. Mora, C. M. Fernandes, J.-J. Merelo, V. Ramos, J. L. J. Laredo, “KohonAnts. A Self-Organizing Ant Algorithm for Clustering and Pattern Classification”, Proceedings of the XI Artificial Life Conference, pp. 428-435 (2008).


I just got back from two congresses. The first one, the International Conference on Evolutionary Computation (ICEC) was held in Albufera, Valencia (Spain) from 24 to 26 of November, as a part of the 2nd International Joint Conference on Computational Intelligence (IJCCI 2010), together with the Int. Conf. on Fuzzy Computation (ICFC) and the Int. Conf. on Neural Computation (ICNC). At ICEC I presented the paper “Investigating Replacement Strategies for the Adaptive Dissortative Mating Genetic Algorithm” (Fernandes, Merelo and Rosa, 2010), which addresses one of the open questions of my PhD thesis.  The abstract:

This paper investigates the effects of modifying the Adaptive Dissortative Mating Genetic Algorithm (ADMGA) replacement strategy on the performance of the algorithm in dynamic problems. ADMGA is a variation of the standard GA with a mating restriction based on the genotypic similarity of the individuals. Dissimilar individuals mate more often than expected by chance and, as a result, genetic diversity throughout the run is maintained at a higher level. ADMGA was previously tested in dynamic optimization problems with promising results: the algorithm shows to outperform standard GAs and state-of-the-art approaches on several problems and dynamics. However, the performance of the algorithm degrades when the frequency of changes increases. Due to the premises under which ADMGA was tested, it has been argued that the replacement strategy that emerges from the algorithm’s dissortative mating strategy may be harming the performance in such situations. This study proposes alternative replacement schemes with the objective of improving ADMGA’s performance on fast changing environments (without damaging the performance on slower ones). The strategies maintain the simplicity of the algorithm, i.e., the parameter set is not increased. The replacement schemes were tested in dynamic environments based on stationary functions with different characteristics, showing to improve standard ADMGA’s performance in fast dynamic problems.


Then I headed for Djerba, Tunisia, to attend the International Conference on Metaheuristics and Nature Inspired Computing (META 2010), where first I presented the preliminary results of one our teams’ most motivating research projects, summarized in the paper “Towards an Automatic Sleep Classification Procedure Using Wavelet Based Feature Extraction” (Mora, Fernandes, Herrera, Castillo, Rosa and Merelo, 2010). In this project, we are using several algorithms — such as Support Vector Machines, G-Prop, K-nn and “our” clustering and classification algorithm, KANTS — to try to solve the extremely difficult problem of automatic sleep stages classification. A more detailed reported will appear soon.

Finally, and still in META, I presented the paper “Optimizing Evolutionary Algorithms at Program Level” (Merelo, Mora, Castillo, Laredo and Fernandes, 2010), an attempt to define good programming practices for Evolutionary Algorithms and identify the most relevant bottlenecks that may appear in this type of code. I leave you the slide presentation of this last paper: