Modeling Artificial Life: A Cellular Automata Approach - Computational Intelligence Techniques for Comparative Genomics - page 79

2.1.3 Hodgepodge Algorithm

Hodgepodge algorithm [ 14 16 ] uses excitable cellular automation to model phys-

ical and chemical systems like oscillations and chemical reactions. Techniques like
neural networks and wave dynamics can be used to increase the ef

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ciency of the
phenomenon. In excitable cellular automaton, study systems with uniform states
which are linearly stable but are susceptible to nite perturbations exist. The cells

within the lattice exist in three states exited state (infected cells), recovering state

(ill cells), and relaxed state (healthy cells). Each cell keeps

—

uctuating in between
the three states on the basis of the states of its neighboring cells. Here, a
S

fl

S square lattice is used with periodic boundary conditions. Mathematically, each
cell is assigned with a value which may range from 0 to k

×

1. A healthy cell is

-

assigned with 0, ill cell is assigned with k 1, and infected cell can be assigned

with any nonzero value lying in between 0 and k

−

1. The basic rules applicable for
this algorithm include the remodeling of biological rules. An ill cell eventually
becomes healthy. A healthy cell can get infected or might remain healthy. An
already infected cell can become more infected. The change in state is calculated
basing on the

−

tness of the cell and its neighborhood.

/* Procedure: Hodge Podge Machine Algorithm */

Initialize the population with random individuals;
Randomize the grid if it were the first generation;
Evaluate the fitness of each individual;
Iterate recursively to form next generations;
{

For each cell in the grid get the infection level of the
cell; Get the state of the cell
{

If Cell = fully ill, make it better;
If Cell = partially ill, make it more sick;
If cell = healthy, see if the neighboring infected cells are enough to make it
ill; } /* evaluate fitness iteratively and determine state */

Construct the cellular grid with next generation;

}

For all the three algorithms stated above, the main design criteria is to choose the
rules carefully such that there is no explosive growth of the cells and there exist
small initial patterns with unpredictable results which shows us a visual plot of the
biological cell division ranging through generations. Simple rules are to be framed
which adhere to the constraints of the biological scenario which is being tried to be
modeled.