Fitness Functions for Ant Colony Paintings

A creativity-support tool for the creation of nonphotorealistic renderings of images is described. It employs an evolutionary algorithm that evolves the parameters governing the behavior of ant species, and the paintings are produced by simulating the behavior of these artificial ants. The design of fitness functions, using both behavioral and image features is discussed, emphasizing the rationale and intentions that guided the design. The analysis of the experimental results obtained by using different fitness functions focuses on assessing if they convey the intentions of the fitness function designer. Introduction Machado and Pereira (2012) presented a non-photorealistic rendering (NPR) algorithm inspired on ant colony approaches: the trails of artificial ants were used 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 trail of each ant is represented by a continuous line of varying width, contributing to the expressiveness of the NPRs. In spite of the potential of this generative approach, the number of parameters controlling the behavior of the ants and their interdependencies was soon revealed to be too large to allow their tuning by hand. The results of these attempts revealed that only a small subset of the creative possibilities allowed by the algorithm was being explored. To tackle this problem, Machado and Pereira (2012) presented a human-in-the-loop Genetic Algorithm (GA) 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 of this creativity-support tool 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. This paper describes 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, 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. Human-in-the-loop in evolutionary art systems are often used as creativity-support tools and thought to have the potential for exploratory creativity. Allowing the users to design fitness functions by specifying desired combinations of characteristics provides an additional level of abstraction, enabling the users to focus on their intents and overcoming the user fatigue problem. Additionally, this approach opens the door for evaluating the system by comparing the intents of the user with the outcomes of the process. We begin with a short survey of related work. Next, in the third section, we describe the system, focusing on the behavior of the ants and on the evolutionary algorithm. In the fourth section we present experimental results, making a brief analysis. Finally, we draw some conclusions and discuss aspects to be addressed in future work.