Optimization of teacher volunteer transferring problems using greedy genetic algorithms

This paper proposes a greedy GA to solve teacher volunteer transferring problems.The proposed GONSGA has better solution scalability than the other two methods.Real-world transferring cases are studied to verify the proposed approach.Compared to the official data, our approach obtains much better results. In this paper, an evolutionary approach based on a greedy genetic algorithm (GA) is studied to serve as an efficient solver for real world teacher volunteer transferring problems (TVTPs). In the proposed approach, the transferring problems are first mathematically formulated into constrained combinational optimization problems and then, an improved neighborhood-search based on greedy search rules is embedded into the mutation operator of the proposed GA method to explore optimal solutions. For verifying the correctness and efficiency of the proposed methods, several real-world transferring cases are studied, and the results show the benefits while adopting the proposed approach in the practical application which can greatly increase the successful transferring numbers comparing to the official TVTP results.

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