Abstract
In the evaluation of technology projects, multiple experts need to be selected to evaluate a group of projects each of which covers different technology fields. However, each expert has his own advantage fields, so it is a significant challenge to find scientifically and automatically a suitable group of experts matching the evaluated projects from a large number of candidates. In this paper, we propose a multi-matching model GES based on genetic algorithm. This model is applied to the established two correlation matrices of “project-field” and “expert-field”, by separately calculating the discrete distribution of projects and experts in the technology field domain. Then GES makes use of the genetic algorithm integrating an evaluation function measuring the matching degree between the projects and the experts to search the optimal candidates. We carried out the throughout experiment based on the realistic electric-power industry data sets, the results show that GES searched effectively and accurately the group of evaluation experts.
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Acknowledgement
This work is supported by the State Grid Corporation of China technology project (SGTYHT/18-JS-206) research and application of assistant decision technology for project approval management based on text mining.
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Cao, T., Xiong, Y., Wang, G., Wei, G. (2021). GES: An Efficient Evaluation Experts Selecting Strategy Based on Genetic Algorithm. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_98
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DOI: https://doi.org/10.1007/978-3-030-70665-4_98
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