Abstract
Recently, many evolutionary computation methods such as GA for multiobjective optimization problems have been reported, and then many objective optimization problems, which have a lot of objective functions, have been especially focused on with the background of the improvement of computer performance. One of the goals in multiobjective optimization problems is to obtain various solutions satisfied with at least one objective function. However, the solutions which have better values in an objective function but not in others are not what users need in practical problems, which makes the multipoint search of MOGA ineffective. This paper investigates the method which enables a user to find practical solutions for him/her with changing the direction of the search interactively, and studies the effectiveness of the proposed method using a practical problem.
