GES: An Efficient Evaluation Experts Selecting Strategy Based on Genetic Algorithm

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.