Exploration of Human Design with Genetic Algorithms as Artistic Medium for Color Images

Abstract

Genetic Algorithms (GAs), a subclass of evolutionary algorithms, seek to apply the concept of natural selection to promote the optimization and furtherance of attributes/features designated by the user. GAs generate a population of chromosomes represented as value strings, score each chromosome with a fitness function on a defined set of criteria, and mutate future generations depending on the scores ascribed to each chromosome. In this paper, each chromosome is a bitstring representing one canvased artwork. Artworks are scored with a variety of design fundamentals and user preference. The artworks are then evolved through thousands of generations and the final art piece is computationally drawn for analysis. GAs have applications in various domains such as hyperparameter tuning, mathematical optimization, reinforcement learning, and black box scenarios. Neural networks are favored presently in image generation due to their pattern recognition and ability to produce new content; however, in cases where a user is seeking to implement their own vision through careful algorithmic refinement, genetic algorithms find a place in visual computing. This paper shows that GAs can act as a medium for artistic expression.