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On Evolving Multi-Pheromone Ant Paintings

On Evolving Multi-Pheromone Ant Paintings Gary Greenfield University of Richmond CEC Evolved Art & Music: July, 2006 Outline Background. Objectives. Multiple-Pheromone Model. (Virtual) Ant Model. Evolutionary Framework. Results. Conclusions. I. Background

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On Evolving Multi-Pheromone Ant Paintings

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  1. On Evolving Multi-Pheromone Ant Paintings Gary Greenfield University of Richmond CEC Evolved Art & Music: July, 2006

  2. Outline • Background. • Objectives. • Multiple-Pheromone Model. • (Virtual) Ant Model. • Evolutionary Framework. • Results. • Conclusions.

  3. I. Background • Ramos et al. (ANTS 2000) – ACO for image processing. • Monmarche et al. (CEC 2003) – Interactive evolution of ant paintings.

  4. Greenfield (EvoMUSART 2005) – Non-interactive ant paintings.

  5. Urbano (EvoMUSART 2005) – Ant paintings using one (environmental) pheromone.

  6. Greenfield (BRIDGES 2006) – Ant paintings using two pheromones.

  7. Model Comparison • Monmarche – Ants leave color trails while seeking luminance. • Greenfield (a) – Ants leave color trails while seeking and avoiding tristimulus colors. • Urbano – Ants seek scent exuded by grid cells. Cells are re-colored by first ant to visit. • Greenfield (b) – Ants seek scent exuded by grid cells and avoid scent exuded by ants. Cells are re-colored by first ant to visit but may be subsequently re-colored.

  8. II. Objectives • Refine ant’s “subsequent re-coloring” ability i.e. improve ant mark making capability. • Evolve ant paintings on the basis of a single trait, the relative locations, or cluster points, of two “species” of ants.

  9. Cluster Point Test (Urbano Model)

  10. Remarks • Non-interactive (image) evolution is one of McCormack’s five open problems in evolutionary music and art. • Ant paintings can be considered from the point of view of the “creativity problem”… Q: Why should ants be able to create paintings? A: Stigmergy - individual ants are rule-based, but collectively their efforts appear to be goal oriented and organized.

  11. III. Multiple-Pheromone Model • Each never visited grid cell emits Pc units of cell pheromone at each time step. • Each ant emits Pa units of ant pheromone at each time step. • E percent of each type pheromone evaporates at each time step. • D percent of each type of pheromone is diffused to the eight neighboring grid cells at each time step.

  12. IV. (Virtual) Ant Model • Ant deposits background color b (white or black according to species) whenever it is first to visit a cell. • Ant maintains current position and current compass heading: N, NE, E, SE, S, SW, W, NW. • Ant senses pheromone levels in each of the three cells in its forward “field of vision.” • Ant moves to the sensed cell with maximum cell pheromone S, if S > T, otherwise ant moves to the sensed cell with minimum ant pheromone s, … AND leaves trail.

  13. Ant Mark Making The “trail” is made by blending a time varying percentage of the ant’s foreground color f with the current cell. viz. Over L time steps an ant may diffuse and blend its foreground color (modulated from, say, f/2 to f ) thereby simulating a “stroke” being painted on a background that was initially re-colored white and black a la Urbano.

  14. Example • 500 ants • 600 x 600 grid • Two species, each initially clustered • Time series after 500, 1000, 1500, 2000, 2500, and 3000 time steps…

  15. V. Evolutionary Framework • M x M grid (M = 200 or 600). • 500 Ants. Na ants use black for background color Nb ants use white for background color Na ~ Nb • Each ant has randomly generated initial offset (Ox,Oy) relative to its species cluster point -- Ca = (Ax, Ay) or Cb = (Bx,By) -- and randomly generated initial direction.

  16. Genetics • Genome: Ca || Cb = (Ax,Ay,Bx,By). • Recombination Operator: Uniform crossover. • Mutation Operator: Genetic drift. • Population Size: P = 8. • Number of Generations: G = 9. • Replacement: Entire population using random pairs formed from P/2 most fit genomes.

  17. Fitness • Ant Painting Termination Condition: Time t, where t is the smaller of 500 times steps or the number of steps until all grid cells have been visited at least once. • Foreground Painting Measure: Let s be the number of times ants made foreground marks during completion of the painting. • Fitness Function: F(Ca || Cb) = st.

  18. Remarks • Fitness is minimized because the objective is to locate the two species “colonies” in such a way that all grid squares are visited in the least amount of time with the least amount of overpainting. • Because of complete replacement the “best” painting may appear in any generation.

  19. VI. Results These two evolved images contrast the aesthetic result we are trying to achieve with the result we are trying to prevent.

  20. From best initial fitness to best fitness over the course of an evolutionary run.

  21. The image with the lowest fitness ever recorded.

  22. The best image from the run that had the most difficulty meeting the fitness objective, and the best image (in an initial population) whose gene line went extinct.

  23. Two images with the same numeric fitness values (their cluster points are parallel translates) but with different aesthetic fitness values.

  24. From the Design and Testing Phase to Show the Potential of the Model

  25. More Evolved Examples

  26. VII. Conclusions • The multiple pheromone model improved the quality of (our) ant paintings. • Non-interactive evolution was able to achieve the design objective. • As a theoretical point, ants could autonomously perform the fitness calculation themselves (“artificial creativity” implications?)

  27. Future Work • Need to “validate” the genetics in these kind of evolutionary art schemes. • Need to better understand how to design fitness functions to extract desired local and global image characteristics. • Need more diverse ant behaviors in the model. • “Preference” testing. (a slippery slope?)

  28. Thank-you!! … … Questions??

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