Incorporating characteristics of human creativity into an evolutionary art algorithm

TL;DR

This paper introduces an evolutionary art algorithm that incorporates human-like creative strategies by using an automatic fitness function to evolve abstract portraits, aiming to mimic human artistic processes and outcomes.

Contribution

It presents a novel algorithm that automates fitness evaluation based on human creative strategies, reducing the need for human intervention in evolutionary art.

Findings

Successfully evolved abstract portraits of Darwin with human-like strategies

Demonstrated the potential for automated fitness functions to emulate human creativity

Enhanced the fluidity of artistic evolution through conceptual network structures

Abstract

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.