Evolutionary algorithms have enjoyed a great success in a variety of different fields ranging from numerical optimization to general creative design. However, to date, the question of why this success is possible has never been adequately determined. In this paper, we examine two algorithms: a genetic algorithm and a pseudo-exhaustive search algorithm dubbed Directed Exhaustive Search. We examine the GA's apparent ability to compound individual mutations and its role in the GA's optimization. We then explore the use of the DES algorithm using a suitably altered mutation operator mimicking the GA's surreptitious compounding of the mutation operator. We find that the DES algorithm is capable of performing comparably to or outperforming the GA over all test problems, as predicted by the theory.