An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.

本研究提出了一种基于强化学习和遗传规划的团队组建方法，在考虑人员和岗位匹配以及团队成员间沟通的情况下，通过验直觉模糊数计算个人工作匹配分数。算法采用集合算法提高解决方案的质量，通过代理人选择搜索模式来实现探索和开发的平衡。基于代理模型的快速评估进一步加速了算法的学习过程，实验验证证实了该算法在真实企业环境中的有效性。

一种强化学习辅助的遗传编程算法，用于考虑人-岗位匹配的团队组建问题