The deterministic Multi-step Crossover Fusion (dMSXF) is an improved crossover method of MSXF which is a promising method of JSP, and it shows high availability in TSP. Both of these crossover methods introduce a neighborhood structure and distance in each permutation problem and perform multi-step searches in the interpolation domain focusing on inheritance of parents' characteristic. They cannot work effectively when parents stand close each other since they search in interpolation domain. Therefore in the case of the MSXF, the Multi-step Mutation Fusion (MSMF), which is the multi-step search in the extrapolation domain, is combined as the supplementary search to improve its search performance. On the other hand, the search mechanism for acquisition of characteristics, such as MSMF, is not applied to dMSXF. In this paper, we introduce a deterministic MSMF (dMSMF) mechanism as complementary multi-step extrapolation search. We apply dMSXF+dMSMF to TSP and JSP, which have structural difference between their landscapes. Through the experiments it was shown that the deterministic multi-step search in interpolation/extrapolation domain performed effectively in combinatorial problems.

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Interactive Evolutionary Computation (IEC) is known as an efficient method which reflects user's subjective preference on media contents. However, user's fatigue caused by repetitive evaluation tasks remains as a severe problem in IEC. The purpose of this study is to propose a new framework of IEC, continuous evaluation-based IEC (CEIEC) that continues user's evaluation tasks in many days. CEIEC allows the user's subjective evaluation day by day to have many evaluation times without severe fatigue, while conventional IECs performed user's task in one time in one day. This study also performed a listening experiment for investigating efficiencies of CEIEC. The target of search with CEIEC was to design a bright sign sound. Sixteen subjects participated in the listening experiment which is composed of tasks of three days: in the 1st and the 2nd days, the subjects performed a selection task throughout fifteen generations. Successive update of the solution was observed during these two days. In the 3rd day, the subjects evaluated three sign sounds with 7-point scale. These three sounds were picked up from the 0th, 14th, and 29th generations, respectively. The sound with the highest score was found in the 29th generation. These results show the efficiencies of CEIEC.

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