Random search can outperform mutation

— Efficient discovery of lowest level building blocks is a fundamental requirement for a successful genetic algorithm. Although considerable effort has been directed at techniques for combining existing building blocks there has been little emphasis placed on discovering those blocks in the first place. This paper describes an analysis of the canonical genetic algorithm that demonstrates a significant weakness in the algorithm and suggests that careful use of random search will lead to better performance than the use of mutation. Experimental results show that this can result in significant performance improvements over the canonical genetic algorithm.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Cameron Skinner, Patricia J. Riddle
• Keywords: Artificial Intelligence, Canonical Genetic Algorithm, CEC 2007, Genetic Algorithm, Successful Genetic Algorithm