A Practice-based Approach for Exploring New Generative Art Schemes Gary R. Greenfield University of...
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Transcript of A Practice-based Approach for Exploring New Generative Art Schemes Gary R. Greenfield University of...
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