| Summary: | 博士 === 國立中央大學 === 資訊管理學系 === 102 === The journal ranking problem has drawn a great deal of attention from researchers in various fields due to its importance in the evaluation of academic performance. Most previous studies solved the journal ranking problem with either a subjective approach based on expert survey metrics or an objective approach based on citation-based metrics. Since both approaches have their own advantages and disadvantages, and since they are usually complementary, this work proposes a brand new approach that integrates the two previous approaches. In addition, the class-ranking is quite valuable method to provide decision makers with the incentive preparation in practice. However, it is a resource allocation and combinatorial optimization, so it is difficult to get results by the traditional citation analysis method. To this end, we propose the second approach in this study to solve the class-ranking with citation-based data.
In this study, we propose two evolutionary PageRank algorithms. The first method uses the Multi-Objective Particle Swarm Optimization to balance citation analysis and expert opinion. Experiments evaluating ranking quality were carried out with citation records and experts’ surveys to show the effectiveness of the proposed method. The results indicate that the proposed method can improve PageRank journal ranking results. The second method uses a tree-based chromosome to represent a class-ranking problem. This encoding can be combining all assigned classes and prestige values in a chromosome effectively. We, also, use the Genetic Algorithm to determine an optimal graded assignment based on the citations and users constraints. Experimental results also proved that this method can be allocation classes precisely, and ensure the similarity between the members of the same class.
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