This study proposes a cooperative evolutionary optimization method between a user and system (CEUS) for problems involving quantitative and qualitative optimization criteria. In a general interactive evolutionary computation (IEC) model, both the system and user have their own role in the evolution, such as individual reproduction or evaluation. In contrast, the proposed CEUS allows the user to dynamically change the allocation of search roles between the system and user, resulting in simultaneous optimization of qualitative and quantitative objective functions without increasing user fatigue. This is achieved by a combination of user evaluation prediction and the integration of interactive and non-interactive EC. For instance, the system performs a global search at the beginning, the user then intensifies the search area, and finally the system conducts a local search in the intensified search area. This study applies CEUS to an image processing filter design problem that involves both quantitative (filter output accuracy) and qualitative (filter behavior) criteria. Experiments have shown that the proposed CEUS can design image filters in accordance with user preferences, and CEUS interacting with a non-naive user enhanced the initial global search so that it converged and found a reasonable solution more than four times faster than a non-interactive search. 
User-system cooperative evolutionary computation for both quantitative and qualitative objective optimization in image processing filter design