Modeling Artificial Life: A Cellular Automata Approach - Computational Intelligence Techniques for Comparative Genomics - page 78

Fig. 4 Game of life [ 17 , 18 ] pattern glider, Langton '

s ant trajectory [ 19 ] after 1,200 generations

cell with exact 3 neighbors becomes alive as if by reproduction. Initial pattern
which is randomized is the seed of the system ' s iterative working [ 18 ] (Fig. 4 ).

2.1.2 Generalized Langton ' s Ant Algorithm

Chris Langton proposed another model of an arti cial life which he named as

'

Langton 21 ]. He opted ant to be an agent who moves by turning left or

right according to the color of the square it is heading onto. The color of the square
changes accordingly. He took a square lattice and de

s ant [ 19 -

ned each square in the lattice
as a cell similar to the game of life algorithm. He also de

ned that each cell has two

states which is determined according to the direction in

which the ant moves. He framed the rules such that if an ant reaches a white square,
it turns right,

“ to-left ” and “ to-right ”

ips the color of square to black, and moves forward. If the ant
reaches a black square, it turns left,

fl

fl ips the color of square to white, and moves

forward. In the rst 100 generations of the algorithm, rather simple and symmetric

patterns are produced. Further patterns get complicated, and irregular patterns are
produced. The ant traces a pseudorandom path until 10,000 steps. Finally, recurrent
highway patterns are produced which repeat inde nitely.

/* Procedure: Generalized Langton's Ant Algorithm */

Construct the Square Lattice;
Create and Initialize the position of the Ant;
Teach Ant How to Behave;
Repeat until end conditions are satisfied;
{ If Square color = Black
{

Turn 90° Right
Set Square Color =White

}
Else
{

Turn 90° Left
Set Square Color =White

}

}