Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Automated Design of Metaheuristic Algorithms: A Survey
Authors: Qi Zhao, Qiqi Duan, Bai Yan, Shi Cheng, Yuhui Shi
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field. |
| Researcher Affiliation | Academia | 1 Southern University of Science and Technology, China 2 Harbin Institute of Technology, China 3 Shaanxi Normal University, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks for its own methodology. It discusses examples of grammar for designing genetic algorithms in Figure 4 and mentions algorithm templates and operators, but it does not present a new algorithm using pseudocode. |
| Open Source Code | No | The paper lists open-source software like irace, Param ILS, SMAC, and Sparkle used in the field of automated algorithm design, and provides links to their repositories. However, these are third-party tools, not code released by the authors for the methodology described in this survey paper. |
| Open Datasets | No | The paper is a survey and does not conduct its own experiments using datasets. It references various numerical benchmark problems and practical problems (e.g., CEC 2005, DTLZ, WFG, NK-Landscape, JSS, TSP, SAT) that have been used in the literature being surveyed, but it does not provide access information for datasets used in its own work. |
| Dataset Splits | No | As a survey paper that does not conduct its own experiments with data, there is no information provided about dataset splits (e.g., training/test/validation splits). |
| Hardware Specification | No | The paper is a survey and does not describe any experimental hardware used for its own research. |
| Software Dependencies | No | The paper is a survey and does not list specific software dependencies with version numbers for its own methodology. |
| Experiment Setup | No | The paper is a survey and does not provide details about an experimental setup, hyperparameters, or system-level training settings. |