The user fatigue problem in interactive evolutionary computation (IEC) is a complex and interesting issue. If the IEC search space is created from the experience or knowledge of domain experts rather than from users values, it causes two potential problems which lead to fatigue problems in IEC: 1) inefficiency and 2) boredom. Therefore, we propose a customer values-based IEC model, solving the fatigue problem by avoiding the potential problems. A case study involving the design of mineral water bottles was used to verify the anti-fatigue capability of the users when using the proposed model. For comparison with the traditional domain knowledge-based model, we built two IEC systems, a customer values-based system and a traditional system, and conducted a user burden test and a system efficiency test over a two-week period. The results of both tests show that our proposed system performed better than the traditional system in designing mineral water bottles.

Genetic algorithm's approach has been one of the most important optimization techniques though the related problems of selection pressure still exist. Diversity is the key issue. For this reason, we explored the implication of redundancy in both genetic algorithm's overrepresentation and multi-explanation. Our research result supports the view of crosscompetition but not diversity loss when considering uniform coding redundancy with expressive elitism and the tolerance of infeasible solutions. On the other side, we developed new decoding procedure based on the inborn searchability of the small world. In designing the network of the reverse logistics having multiple lower-dimensional constraints, with the help of multiple overlapped fitness functions, the performance of genetic algorithms can truly be improved.

Researchers usually focused on using unstructured documents, such as news, scientific literatures, World Wide Web pages, business reports, as input documents of KeyGraph. But the unstructured documents might results in unclear keywords and meaningless keywords. Although traditional document preprocessing could dismiss some of the problems, generating complex and unreadable KeyGraph diagrams were not avoidance. In this research, we applied the concept of chance discovery and KeyGraph to discover hidden blue ocean strategy (BOS) for decision makers who not necessary familiar with the concerned domains. A preprocessing strategy, including develop an operational framework of BOS and design a sentence structure for representing BOS, was proposed to assure the necessary keywords be included in the required documents. Seventy-two documents concerning blue ocean firms were collected as sample cases for developing the operational framework. At last, three experiences were designed for confirming the performance of the proposed preprocessing strategy. Experimental results shown that the subjects were not consistently recognized BOS with traditional approach (used unstructured documents as input documents of KeyGraph). In contrast, the subjects could easily find explicit scenarios of BOS, and recognize a few of implicit scenarios appeared on KeyGraph diagrams with the proposed strategy.

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