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.

翻译：高效团队对于公司成功完成新项目至关重要。为解决考虑人岗匹配的团队组建问题（TFP-PJM），构建了0-1整数规划模型，该模型综合考虑人岗匹配度及团队成员沟通意愿对团队效率的影响，其中人岗匹配度通过直觉模糊数计算。随后，提出一种强化学习辅助的遗传规划算法（RL-GP）以提升解的质量。RL-GP采用集成种群策略：在每代种群进化前，智能体根据获取的信息从四种种群搜索模式中选择一种，从而在探索与开发之间实现良好平衡。此外，算法中引入代理模型评估个体生成的组建方案，加速算法学习过程。通过系列对比实验验证RL-GP的整体性能及算法内改进策略的有效性。通过高效学习获得的超启发式规则可作为组建项目团队时的决策辅助工具。本研究揭示了强化学习方法、集成策略及代理模型在遗传规划框架中的应用优势。搜索模式的多样性与智能选择，以及快速适应评估能力，使得RL-GP能够部署于真实企业环境。