A continuous age model of genetic algorithms applicable to optimization problems with uncertainties

We study a method of optimum seeking in an uncertain environment by extending the conventional genetic algorithms. So far, we have extended genetic algorithms by introducing an age structure, where the key point 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. We newly introduce a continuous age model of genetic algorithms to accumulate the values adequately. Then, the effectiveness of the proposed method is investigated through some computational experiments. As a result, it has been shown that the stability is higher and the possibility of falling into local optimum is lower in using the new age model than in using the former one.