Abstract
These days, requirements of functions tend to spread and the designers need to treat problems as multi-objective optimization. However, decision making through multi-objective optimization is not that simple and we need to give preference of the designer to give a solutions, which has been done through local information called trade off ratio. Usually, trade-off ratio and the results does not have linear relationships, thus it is quite hard to give desired trade-off ratio in a few steps. In the case there are a few objective functions, we can visualize so called Pareto solutions. Then, the designer can grasp the whole relationships of objective functions and it is useful information, something like a map in the whole relationships of objective functions and it is useful information, something like a map in exploring. For that purpose, multi-objective genetic algorithms are quite powerful tools, and there are tons of studies for it. We have been proposing a method to use Data Envelopment Analysis as estimating Pareto Optimality. In this study, we use Genetic Range Genetic Algorithms, and give a new range around obtained Pareto solution and between them. In order to show the effectiveness of the proposed method, we showed some simple example with and without ill setting.
