A Practice-based Approach for Exploring New Generative Art

Schemes

Gary R. GreenfieldUniversity of RichmondRichmond, Virginia, USADecember, 2005 GAP ‘05

Outline

The ubiquity of GA schemes Standard search paradigm Non-interactive genetic algorithms Our practice-based approach Conclusions

The ubiquity of GA schemes

Biomorphs – Dawkins ’89 Evolving Expressions – Sims ’91 Mutator - Todd & Latham ’92 Fractals – Sprott ’96 Strange Attractors – Krawczyk ’03 Polynomiography – Kalantari ’03 Self-similar Tilings – Priebe ’00

Spiral Tilings – Palmer ’05 Hyperbolic Spirals – Dunham ’03 Bar Grids – Roelofs ’04 Spirolaterals – Krawczyk ’00 Component Sculptures – Hart ’03 Fermat Spirals – Krawczyk ’05 TSP Art – Galanter ’04

TSP Art – Kaplan & Bosch ’05 Polar Transformations – Bleicher ’04 Reaction Diffusion – Behravan &

Carlisle ’04 ACO – Aupetit et al ’03 Cellular Morphognesis –

Eggenberger ’97

Standard search paradigm

Interactive genetic algorithm IGAIGA- slow and cumbersome- subject to user fatigue

- “novelty” generator? (Dorin ’01)- aesthetic intent? (McCormack ’05)

Evolving Expressions IGA (Greenfield ’92-’96)

Modeled after Sims User interface to control genetics Features

- Node iteration - External image acquisition- Palette management- 2D and 3D imaging

Slippage I-III

Eerily Slow

Expressionism IV

Re-coloring Example

A

Non-interactive genetic algorithms

Baluja et al ’94 Rooke ’98 Machado & Cardoso ’98

Open Problem :Open Problem :

To derive fitness functions that are capable of measuring human aesthetic properties of phenotypes. (McCormack ’05)

A fitness function taxonomy

Positive Feedback- simulated co-evolution - neural nets (Cardoso et al ’98)

Negative Feedback- simulated immune systems (Romero et al ’05)- simulated diseases (Dorin ’05)

Direct Control- user-designed

Indirect Control- multi-objective optimization- ant colony optimization

Learning - image analogies (Hertzmann et al ’01)- simulated gaze data

Our practice-based approach

Co-evolutionary framework (’00) Color segmentation analysis (’02) Multi-objective Optimization (’03) Virtual ant paintings (’05) Cellular morphogenesis (’05) Serial polar transf. motifs (’05)

Co-evolutionary framework (’00)

Host-parasite mechanics

Fitness calculation

Color segmentation analysis (’02)

Images with segmentations

Fitness is responsive to the segmentation geometry

Multi-objective Optimization (’03)

Virtual ant paintings (’05)

Fitness using arithmetic expressions of exploitation (Nf) and exploration (Nv) measurements

Fitness ~ Nf / Nv

Fitness ~ Nf + Nv

Fitness ~ (Nf) (Nv)

Fitness ~ Nv

Cellular morphogenesis (’05)

The Void Series

Serial polar transformation motifs (’05)

Randomly generated

Evolved motifs

Fitness ~ min or max of “boundary” pixel count (genome “length” fixed)

Most, average, and least fit for a min run

Another “min” run

A “max” run

Detail

Simulated Robot Paintings ’05

Performance measurementsNp - # squares paintedNb - # forward collisionsNs - # couldn’t moveNc - # color sense hits

Fitness ~ weighted linear comb.

Fitness ~ (Nb)(Nc)

Fitness ~ (Ns)(Nc) + (Np)(Nb)

Conclusions

Devise appropriate image assessment parameters

Consider fitness function taxonomy Rely on practice-based

implementation

Thank-you!

ggreenfi@richmond.edu

http://www.mathcs.richmond.edu/~ggreenfi/