Abstract: |
Evolutionary algorithms have enjoyed a great success in a variety\nof different fields ranging from numerical optimization to general creative\ndesign. However, to date, the question of why this success is possible has\nnever been adequately determined. In this paper, we examine two algorithms,\na genetic algorithm and a pseudo-exhaustive search algorithm dubbed Directed\nExhaustive Search. We examine the GA\'s apparent ability to compound individual\nmutations, and its role in the GA\'s optimization. We then explore the use\nof the DES algorithm using a suitably altered mutation operator mimicking the\nGA\'s surreptitious compounding of the mutation operator. We find that the\nDES algorithm is capable of performing comparably to or outperforming the GA \nover all test problems, as predicted by theory. |
