Abstract
We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.
Highlights
Evolutionary art is a branch of generative art, which is automatically generated by evolutionary computation
We propose a pixel-based evolution method for creating evolutionary art
The main motivation of our work is to provide a supplement to existing evolutionary art generation methods
Summary
Evolutionary art is a branch of generative art, which is automatically generated by evolutionary computation. Evolutionary computation can be used to create various artworks, including 2D artworks, Approaches for evolutionary art can be classified into two categories, agent-based methods and pixel-based methods. The evolutionary art will be created with the agent’s evolution iterations. Another type of these approaches designed some non-figurative agents that can receive an image as a self-organizing map [8]. The proposed method, named biomorphs, evolved 2D shapes made up of straight black lines. The biomorphs, as some of the earliest evolutionary artworks, were applied to 3D models by Todd et al The method was called the interactive genetic algorithm, allowing people to manually select the mutant individuals [11]. There are many approaches used efficient and autonomous methods such as deep learning, neural networks, and convolutional neural networks [14,15,16]
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