Incorporating chromosome differentiation in genetic algorithms

A genetic algorithmic methodology, termed a genetic algorithm with chromosome differentiation (GACD), is described which incorporates chromosome differentiation for evolutionary process. Chromosomes are distinguished into two categories of population over the generations based on the value contained in the two class bits. These are initially generated based on maximum hamming distance between them. Crossover (mating) is allowed only between individuals belonging to these categories. Theoretical analysis shows that the basic tenet of genetic algorithms holds for GACD as well; above average, short, low order schema will receive increasing number of trials in subsequent generations. It is also shown that in certain situations, the lower bound of the number of instances of a schema sampled by GACD is greater than or equal to that of the conventional genetic algorithm. Experimental results on a large number of function optimization and pattern classification problems demonstrate the significantly better performance of GACD over the conventional ones. (C) Elsevier Science Inc. 1998.