published in the Proceedings of
About the Java Simulation:
The simulation will run for 200,000 time-steps. The speed of the simulation depends on the computer. Running on a faster computer is recommended, if possible.
Most of the resulting dynamics will be of the "Brain" variety, having various forms of gliders and linear waves propogating at the speed of light in all four orthogonal directions. Other varieties of glider-rich CA dynamics can evolve as well.
The white dots are the particles. The cells in the CA are colored differently for each state, as determined by genes. The quiescent state is shown as gray. Although these color values are genetic, they do not have any evolutionary purpose - they are there to serve human eyes only.
The Graph
The graph just underneath the simulation shows the progress of the swarm over the span of the 200,000 time steps. What it shows is the value of "average ride quality" of the particles WHICH ARE RIDING. In other words, if a particle is being attracted to a cluster of live cells, it is said to be "riding" on that cluster. If that cluster of live cells is moving at a coherent velocity, the particle measures this as a "good ride", and then rewards the local region of the CA with selection and reproduction. When the graph is updated (every 500 time steps) the "ride quality" of each particle that is riding is measured and averaged into one single value. This value is what is plotted in the graph. Average ride quality increases as gliders emerge.
Symbols Visualizing the Genetic Operators
A toggle button is provided for turning on or off the view of the genetic operators that the particles perform. These are as follows:
Selection - a particle selects a copy of the genes of its reference cell when it has detected a superior ride. This is
shown as a dot appearing at the location of the particle.
Reproduction - All particles continually deposit copies of the genes they carry around back into the CA space. When
depositing genes, crossover is used, such that contiguous sections of the gene sequence is copied onto a Moore
neighborhood surrounding the reference cell. This is shown as a horizontal line appearing at the location of the particle.
Exchange - When two cells come in contact with each other (reach within a cell's-width of distance), there is a random
chance of exchanging genetic information. The particle which has detected a superior ride gives a copy of its genes to
the other particle. This is shown as a vertical line appearing at the location of the particle.
Mutation - Every cell has a (rare) chance of having one of its genes randomly changed. This is shown as an 'x' which
appears at the location of the particle.
The Configuration Parameters
Num states - the number of possible states a cell can have.
Num rules - the transition function of a cell uses a sequence of semi-totalistic rules - some of these rules may cancel-out others when the sequence is applied, due to the unpredictable nature of the random initialization, and the effects of the genetic operators. More rules causes denser dynamics, less quiescence, and more chaos at initialization. However, the swarm always evolves these rules back towards the edge between chaos and order.
Resolution - the number of cells in either dimension. Total number = resolution squared.
Max count - the transition function uses the counting scheme. For any given rule in the transition function, the highest number of neighbors (of a specific state) it can count is this number.
Wave period - This is the period of time between instances of the "Noise Wave", which introduces non-quiescent random cell states in order to stir the soup.
Num Particles - the number of particles in the swarm.
Mutation - The genes that a particle is carrying at any given time has a small chance of mutating. This is the value that determines how often a random mutation is likely to occur.
Crossover - Crossover rate determines the size of the genetic building blocks that are deposited from the particle to its neighborhood in the CA.
Deposit Rate - This number determines the random chance of a particle depositing a copy of its current genes into the CA. When it deposits these genes, it uses crossover to randomly distribute contiguous chunks into the Moore neighborhood surrounding it.
Exchange Rate - This number determines the random chance of a particle exchanging genetic information with another particle which it has come in cantact with. The particle with the highest "best ride" value gives its genes to the other particle.
Re-birth Rate - This number determines the random chance of a particle re-appearing at a new random location, and having its memory erased.
Inertia Weight - This is a constant dampening of each particle's velocity at every time step.
Attraction - This is the strength of the force applied to a particle when there is at least one non-quiescent cell in it's 5X5 neighborhood.
Jitter - When a cell is not "riding", it meanders about randomly, in a brownian manner. This number determines the strength of the random force.
Evolving Conway's Game of Life
If you select
, and then 
, the simulation
will be re-initialized with a few parameters changed from their default settings (number of states is set to
2, number of rules is set to 6, and wave weight is set to 0.5). Most likely, it will converge on Conway's
Game of Life. Convergence is slow, and ride quality is not great.
The Selection Criterion
The selection criterion measures a certain aspects of a particle's velocity so that it can evaluate the quality of its ride (when it is being attracted to non-quiescent cells in its local neighborhood). The selection criterion is a real number value defined as:
(|v| - |d|) / C
-where v is velocity; d is the difference in velocity since the last time step, and C is the speed of light (the equivalent of the width of one cell). The result is clamped so as not to exceed 1.0 or to fall below 0.0, however, due to the existence and tuning of the friction and attraction constants, normally this value would rarely fall outside of the interval of 0-1.
(c) copyright 2005, by JJ Ventrella