Introduction

The term ``genetic algorithm'' (GA) is applied to any search or optimization algorithm that is based on Darwinian principles of natural selection. A key concept in genetic algorithms is that of a chromosome. A chromosome contains a group of numbers that completely specifies a candidate during the optimization process. For example, when finding the root of a polynomial, the candidates are complex numbers. One choice of chromosome could consist of two numbers - the real part and the imaginary part - to completely specify a candidate.<sup>2</sup> One can ``crossover'' two chromosomes (the ``parents'') to get one or more ``children''. The numbers that specify a child are then taken from one or the other parent. For example, a child of two parents $3+2i$ and $5+7i$ could be $3+7i$. A single chromosome can be cloned, a process known as ``reproduction''. In this case, the numbers that specify a child are identical to those of the parent; $3+2i$ after reproduction yields another chromosome that represents the candidate root $3+2i$. A chromosome can also be ``mutated''. In this process, the numbers corresponding to the chromosome are perturbed slightly, to yield somewhat different numbers. A chromosome representing $2+3i$, after mutation, could represent $2+3.07i$. Typically, genetic algorithms use crossover, mutation and reproduction to provide structure to a random search.

GAs use randomization heavily in choosing individuals (or ``genetic'' parts of individuals) who will be propagated to future generations. In the work described in this paper, we used a modified version of the simple genetic algorithm described in Goldberg (1989).