Abstract
In this paper, we propose a method of optimum seeking in an uncertain environment by extending the conventional genetic algorithms (GA). The key point of our approach is to evaluate an individual not directly by an objective value of a corresponding solution currently observed, but by accumulating values which have been observed at preceding generations. Finally, we confirm the effectiveness of our extended GA through some computaitional experiments using simple function optimization problems.
