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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |