  This paper describes a method that automatically searches Artificial Neural
Networks using Cellular Genetic Algorithms. The main difference of this method
for a common genetic algorithm is the use of a cellular automaton capable of
providing the location for individuals, reducing the possibility of local
minima in search space. This method employs an evolutionary search for
simultaneous choices of initial weights, transfer functions, architectures and
learning rules. Experimental results have shown that the developed method can
find compact, efficient networks with a satisfactory generalization power and
with shorter training times when compared to other methods found in the
literature.
