A Practice-based Approach for Exploring New Generative Art Schemes

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

13. Re-coloring Example

14. A

15. 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)

16. 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

17. 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)

18. Co-evolutionary framework (’00)

21. Host-parasite mechanics

22. Fitness calculation

23. Color segmentation analysis (’02)

24. Images with segmentations

25. Fitness is responsive to the segmentation geometry

27. Multi-objective Optimization (’03)

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

32. Fitness ~ Nf / Nv

33. Fitness ~ Nf + Nv

34. Fitness ~ (Nf) (Nv)

35. Fitness ~ Nv

38. Cellular morphogenesis (’05)

39. The Void Series

44. Serial polar transformation motifs (’05)

45. Randomly generated

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

47. Another “min” run

48. A “max” run

49. Detail

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