Solving Overlapping Coalition Structure Generation in Task-Based Settings
Authors: Guofu Zhang, Zhaopin Su, Xiaoxiao Song, Zixuan Gao, Miqing Li, Xin Yao
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental results and address the research questions raised in Section 7.1. |
| Researcher Affiliation | Academia | Guofu Zhang EMAIL Zhaopin Su EMAIL (Corresponding Author) Xiaoxiao Song EMAIL Zixuan Gao EMAIL School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei, Anhui, China Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei, Anhui, China Miqing Li EMAIL CERCIA, School of Computer Science, University of Birmingham, Birmingham, UK Xin Yao EMAIL School of Data Science, Lingnan University,Hong Kong SAR, China CERCIA, School of Computer Science, University of Birmingham, Birmingham, UK |
| Pseudocode | Yes | Algorithm 1 Checking the feasibility of a disjoint (C, V ). |
| Open Source Code | Yes | To ensure verifiability, we provide the source codes of the compared methods and the raw instances at the following link: https://github.com/zgfhfut/OCSG. |
| Open Datasets | Yes | Therefore, we generated test instances via simulations of some class of OCSGP. Following the existing work, we generated at total 150 different instances randomly from a normal distribution in the above two settings on the basis of n [10, 100], m [6, 24], r [1, 24], bj k [0, 300], di k [0, 450], and µi [50, 100]. ... To ensure verifiability, we provide the source codes of the compared methods and the raw instances at the following link: https://github.com/zgfhfut/OCSG. |
| Dataset Splits | No | The paper describes generating test instances for different scenarios, but does not specify how a larger dataset was split into training, testing, or validation sets. Instead, it mentions generating specific instances and running experiments on them repeatedly. |
| Hardware Specification | Yes | All the codes of the compared methods were written in C++ and run on a computer with Intel Xeon E5 2.20 GHz CPU, 32.0 GB of RAM, and Windows Server 2012. |
| Software Dependencies | No | The paper mentions that the codes were written in C++, but does not provide specific version numbers for any libraries, compilers, or other software dependencies. |
| Experiment Setup | Yes | We adopted the recommended parameter settings in (Liu et al., 2016; Su et al., 2020; Zhang et al., 2020). Specifically, the maximum number of fitness evaluation is 1,500. In GA, the crossover rate is 0.9 and the mutation rate is 0.1. For BPSO, both of the two learning factors are set to 2.0 and the maximum velocity is restricted to 5.0. As for BDE, the crossover probability is 0.25 and the scaling factor is 1.0. |