Genetic algorithms in noisy environment

Abstract

Genetic Algorithms (GA) have been widely used in the areas of function optimization and machine learning. In many of these applications, the effect of noise is a critical factor in the performance of the genetic algorithms. In this paper, we propose an effective method for obtaining the optimal solution by using an optimal solution list and systematically changing certain parameters of the algorithm. Our results show that the optimal solution list is able to provide a small solution set that contains near optimal solutions.