A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

2023-04-08 · Yangyang Guo, Hao Wang, Lei He, Witold Pedrycz, P. N. Suganthan, Yanjie Song

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.

📄 PDF Abstract BibTeX arXiv:2304.04022

Code (0)

등록된 구현이 없습니다.

Tasks

Decision Making reinforcement-learning

Neurogenetic Programming Framework for Explainable Reinforcement Learning

2021-02-08 · Vadim Liventsev, Aki Härmä, Milan Petković

Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of p…

OpenAI Gym reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Interpretable Policies for Reinforcement Learning by Genetic Programming

2017-12-12 · Daniel Hein, Steffen Udluft, Thomas A. Runkler

The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if th…

regression reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming

2025-08-13 · Vasileios Saketos, Sebastian Kaltenbach, Sergey Litvinov, Petros Koumoutsakos arxiv

Algorithmic discovery has traditionally relied on human ingenuity and extensive experimentation. Here we investigate whether a prominent scientific computing algorithm, the Kalman Filter, can be discovered through an aut…

Surrogate-Assisted Genetic Programming with Rank-Based Phenotypic Characterisation for Dynamic Multi-Mode Project Scheduling

2026-03-17 · Yuan Tian, Yi Mei, Mengjie Zhang arxiv

The dynamic multi-mode resource-constrained project scheduling problem (DMRCPSP) is of practical importance, as it requires making real-time decisions under changing project states and resource availability. Genetic Prog…

Case Study: Using AI-Assisted Code Generation In Mobile Teams

2023-08-09 · Mircea-Serban Vasiliniuc, Adrian Groza

The aim of this study is to evaluate the performance of AI-assisted programming in actual mobile development teams that are focused on native mobile languages like Kotlin and Swift. The extensive case study involves 16 p…

Code Generation Management