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C.M. Fernandes12
C. Isidoro2
F. Barata2
J.J. Merelo1
A.C. Rosa2
From Pherographia to Color Pherographia
Color Sketching with Artificial Ants
1University of Granada2Technical University of Lisbon
Summary
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20112
The Ant System: from Chialvo and Millonas’s Ant Model to Pherographia. Artwork created with monochromatic pherographia. Color Pherographia (four variations). Results. Conclusions and future work.
Original b/w photo Pheromone density after 100 iterations
Ants after 100 iterations
The Original Model
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20113
Dante Chialvo and Mark Millonas, How Ants Build Cognitive Maps, 1995 The model simulates a swarm of ants moving in an homogeneous environment.
A population of ants is randomly distributed in a two-dimensional array. The ants move one step (cell) in each iteration, following simple rules.
Global and complex behaviour emerges from the simple rules and from the indirect interaction of the ants via the environment.
Self-Organization Stigmergy Simple ((local, no explicit memory,
homogeneous and isotropic)
The Original Model (Chialvo and Milonas)
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20114
Pheromone weighting function:
1
1W
Normalised Transition probabilities (probability to go from cell k to cell i)
kj jj
iiik wW
wWP
Pheromone deposition rate: T= η
increasing the cell’s pheromone density σ
β: degree of randomness with which the ants follow the pheromone gradientδ: saturation effect, ant’s ability to sense pheromone decreases at high concentrations
w = 1/12
w = 1/4
w = 1/2w = 1/2
w = 1/20
w = 1
w = 1/4
w = 1/12
Evaporation: pheromone density in each cell is decreased by a constant amount.
The Original Model
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20115
Ramos and Almeida’s Model
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20116
- Instead of constant pheromone deposition rate, a term not constant is included:
cmpT
Gives a measure of similaritybetween two different lattice
windows, in terms of grey levelspatial arrangement.
Constant
Ramos and Almeida, Artificial Ant Colonies in Digital Images Habitat, ANTS 2000
Final Model
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20117
An “evolutionary” component is added to the ant system.Each ants has an initial energy that decreases in each time step:
the probability of surviving depends on the energy: P = 1-e(a)Each ant is allowed to reproduce in each time step. The
reproduction probability depends on the number of neighbouring ants and the pheromone density.
Fernandes, Ramos and Rosa, Self-Regulated Artificial Ant Colonies on Digital Image Habitats, International Journal of Lateral Computing, 2005
)max
)1((*)(
gl
gl
r nWP
W(0) = W(8) =0; W(4) = 1; W(5) = W(3) =0.75; W(6) = W(2) =0.5; W(7) = W(1) = 0.25.
Timor Mortis Conturbat Me (2008)
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20118
“Timor Mortis...” was exhibited at the P4Photography art gallery, in Lisbon.About this work: http://carlosmfernandes.com/index_archivos/Page768.htm
Eadweard Muybridge (1830-1904)
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 20119
The Horse and the Ants (2009)
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201110
The Horse and the Ants has been exhibited in several art and science shows.
Criatividade Computacional, ISCTE, Abril de 2010
11
Studies for a Modern Zoetrope (2011)
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201112
Becher’s typologies: analysis and synthesis
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201113
Bernd e Hilla Becher
Idris Kahn
Becher’s tipologies: analysis and synthesis
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201114
Color Pherography
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201115
The main difference is the equation:
cmpT In b/w pherography, ∆ measures the contrast in the region around the ant’s position.
In color pherography: First, RBG is converted to L, a and b (Lab color space)
L measures the lightness a and b measure the color.
∆ is the Euclidean distance between the average of L, a and b, each averaged over the cell and its 8 neighborig cells, and the average of L, a and b averaged over the previous cell and its 8 neighboring cells.
Color Pherography
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201116
Four variations were tested, each one with different rules for the ants movements and for handling occupied cells
Originally, the objective was to remove a bias introduced by constraint of having no more than one ant in each cell.
Color Pherography
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201117
Variation 1: when an ant tries to move to an occupied cell, the ant that occupies that cell is moved in the same direction and its directional vector changes.
Color Pherography
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201118
Variation 2: the ants are not allowed to move to occupied cells. If one tries to move to an occupied cell, it will stop, and the direction of the ant occupying that cell changes to that of the vectorial sum between the original direction and the one towards which this ant tried to move.
Color Pherography
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201119
Variation 3: ants “bounce off” if they try to move to occupied cells.
Color Pherography
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201120
Variation 4: introduces speed and implements ants with variable speed, that is, the speed may change when an ant moves to a cell occupied by another ant.
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201121
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201122
Conclusions and Future Work
Congress on Evolutionary Computation, CEC’11, New Orleans, USA, 201123
The monochromatic pherographia has been extended to color pherographia.
Color pherographia also detects the edges of the image.
Investigate other local and simple rules that generate different global behaviour.
Emergence, memory and readaptation (videos).
Implement pherographia (B/W and color) in a digital camera using LUA programming language.