I. Introduction
The genetic algorithm is one of many techniques used to find appropriate solutions for optimization problems. The GA tries to mimic the process of biological evolution where, over successive generations, individuals who are best suited to survive in an environment live on and reproduce, while other individuals die off [1]. Eventually, over many generations, the population has been optimized and only individuals who are suited to the environment live on. The GA tries to generate a viable solution to a problem through the successive evolution of substandard solutions.