Gary R. Greenfield

Evolving Expressions and Art by Choice

One common criticism of algorithmic art is its slavish devotion to technical virtuosity at the expense of artistic intent and content. To address this problem, the author uses an algorithmic method known as evolving expressions, which both challenges the technical ability of the artist and also paves the way to art by choicean art that re-creates what lies in the imagination by visualizing the creatures that live there, the creatures of our dreams.

Computational aesthetics as a tool for creativity

We consider the problem of designing software tools that automatically evaluate the aesthetic content of images. Our purpose is to enhance the creative potential of generative art systems. We propose a number of meta-rules for enhancing creativity.

On Evolving Multi-Pheromone Ant Paintings

The use of swarm simulations and ant colony optimization methods for making abstract art has a brief but interesting history. We introduce some new techniques for evolving ant paintings using a non-interactive genetic algorithm. Key features of our virtual ant simulation model include: (1) the use of multiple pheromones, (2) the reliance on stigmergy to determine when an ant painting is finished, (3) ant interaction and exploration dynamics that lead to the formation of a background image and an overpainting, and (4) the design of a fitness function to guide the evolution of ant paintings subject to certain aesthetic goals. We show examples of our evolved ant paintings.

Simulated Aesthetics and Evolving Artworks: A Coevolutionary Approach

The application of artificial-life principles for artistic use has its origins in the early works of Sommerer and Mignonneau, Sims and Latham. Most of these works are based on simulated evolution and the determination of fitness according to aesthetics. Of particular interest is the use of evolving expressions, which were first introduced by Sims. The author documents refinements to the method of evolving expressions by Rooke, Ibrahim, Musgrave, Unemi, himself and others. He then considers the challenge of creating autonomously evolved artworks on the basis of simulated aesthetics. The author surveys what little is known about the topic of simulated aesthetics and proceeds to describe his new coevolutionary approach modeled after the interaction of hosts and parasites.

Avoidance Drawings Evolved Using Virtual Drawing Robots

We introduce a generative system for “avoidance drawings”, drawings made by virtual drawing robots executing a random walk while simultaneously avoiding the paths of other robots. The random walk method is unique and is based on a curvature controlling scheme initially introduced by Chappell. We design a fitness function for evaluating avoidance drawings and an evolutionary framework for evolving them. This requires us to follow principles we elucidate for simulated evolution where the generative system is highly stochastic in nature. Examples document the evolutionary system’s efficacy and success.

Ant-and ant-colony-inspired alife visual art

Ant- and ant-colony-inspired ALife art is characterized by the artistic exploration of the emerging collective behavior of computational agents, developed using ants as a metaphor. We present a chronology that documents the emergence and history of such visual art, contextualize ant- and ant-colony-inspired art within generative art practices, and consider how it relates to other ALife art. We survey many of the algorithms that artists have used in this genre, address some of their aims, and explore the relationships between ant- and ant-colony-inspired art and research on ant and ant colony behavior.

A platform for evolving controllers for simulated drawing robots

We investigate the problem of evolving controllers for simulated drawing robots. We describe the primitive assembly language that we designed to use for the genome for a drawing robot in order to facilitate controller evolution, as well as the corresponding structure and execution of the decision tree phenotype it supports. Our controllers are modeled after controllers that have been evolved for video games such as Mario Bros. We present some preliminary examples of evolved controller robot drawings.

Generative art and evolutionary refinement

In considering a case study, we examine the process of promoting emergence and creativity within an evolutionary art system using the technique of evolutionary refinement. That is, for the complex, difficult to predict generative scheme based on a model for simulating cellular morphogenesis that forms the generative component of an evolutionary art system, we discuss how we proceed in stages to develop, analyze, focus, and re-target evolved genetic material for aesthetic purposes — in this instance aesthetic pattern formation.

Evolved Look-Up Tables for Simulated DNA Controlled Robots

We describe our efforts to convert (short) DNA sequences obtained from the NCBI library into control sequences for simulated robots by simultaneously evolving both a look up table to assign codons to robot commands and a look up table to assign codons to numerical values that serve as arguments to those commands. Our simulated robot is loosely modeled after the Khepera robot. When the robots sensing capabilities are disabled, we are provided with a sophisticated turtle graphics platform. We formulate a fitness function for evaluating the drawings obtained from codon look up tables and we make inter-DNA and intra-DNA comparisons using our evolved tables. Our results suggest that information content can only be weakly extracted from DNA in this way.

Designing metrics for the purpose of aesthetically evaluating images

The algorithmic and evolutionary art movements within computer-generated art have helped spur interest in evaluating images on the basis of their aesthetic merit. When attempting to use non-interactive techniques to address this issue, two problems arise: (1) designing metrics that have explicit computational representations, and (2) establishing that such metrics actually fulfill their intended purpose. We survey our experiences in designing metrics for non-interactively guiding image evolution to obtain aesthetic images and we propose a taxonomy for metric frameworks. We also discuss some issues relevant to validating such metrics.