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A Practice-based Approach for Exploring New Generative Art Schemes

A Practice-based Approach for Exploring New Generative Art Schemes. Gary R. Greenfield University of Richmond Richmond, Virginia, USA December, 2005 GAP ‘05. Outline. The ubiquity of GA schemes Standard search paradigm Non-interactive genetic algorithms Our practice-based approach

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A Practice-based Approach for Exploring New Generative Art Schemes

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  1. A Practice-based Approach for Exploring New Generative Art Schemes Gary R. Greenfield University of Richmond Richmond, Virginia, USA December, 2005 GAP ‘05

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

  3. 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

  4. 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

  5. TSP Art – Kaplan & Bosch ’05 • Polar Transformations – Bleicher ’04 • Reaction Diffusion – Behravan & Carlisle ’04 • ACO – Aupetit et al ’03 • Cellular Morphognesis – Eggenberger ’97

  6. Standard search paradigm • Interactive genetic algorithm IGA- slow and cumbersome- subject to user fatigue- “novelty” generator? (Dorin ’01)- aesthetic intent? (McCormack ’05)

  7. 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

  8. Slippage I-III

  9. Eerily Slow

  10. Expressionism IV

  11. Re-coloring Example

  12. A

  13. Non-interactive genetic algorithms • Baluja et al ’94 • Rooke ’98 • Machado & Cardoso ’98Open Problem : • To derive fitness functions that are capable of measuring human aesthetic properties of phenotypes. (McCormack ’05)

  14. 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

  15. 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)

  16. Co-evolutionary framework (’00)

  17. Host-parasite mechanics

  18. Fitness calculation

  19. Color segmentation analysis (’02)

  20. Images with segmentations

  21. Fitness is responsive to the segmentation geometry

  22. Multi-objective Optimization (’03)

  23. Virtual ant paintings (’05) • Fitness using arithmetic expressions of exploitation (Nf) and exploration (Nv) measurements

  24. Fitness ~ Nf / Nv

  25. Fitness ~ Nf + Nv

  26. Fitness ~ (Nf) (Nv)

  27. Fitness ~ Nv

  28. Cellular morphogenesis (’05)

  29. The Void Series

  30. Serial polar transformation motifs (’05)

  31. Randomly generated

  32. Evolved motifs • Fitness ~ min or max of “boundary” pixel count (genome “length” fixed) • Most, average, and least fit for a min run

  33. Another “min” run

  34. A “max” run

  35. Detail

  36. Simulated Robot Paintings ’05 • Performance measurementsNp - # squares paintedNb - # forward collisionsNs - # couldn’t moveNc - # color sense hits

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