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

Genetic Algorithms (GAs), a subclass of evolutionary algorithms, seek to apply the concept of natural selection to promote the optimization and furtherance of “something” 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 project, each chromosome is a bitstring representing one canvased color 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 piece is computationally drawn for analysis. While the rise of gradient-based optimization has resulted in more limited use-cases of GAs, genetic algorithms still have applications in various settings 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 still find a place in visual computing.