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..
Choosing the Right Algorithm With Hints From Complexity Theory
Authors: Shouda Wang, Weijie Zheng, Benjamin Doerr
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show a remarkably good performance of the Metropolis algorithm, clearly the best of all algorithms regarded for reasonable problem sizes. |
| Researcher Affiliation | Academia | 1 Laboratoire d Informatique (LIX), Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, France 2 Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China |
| Pseudocode | Yes | Algorithm 1: Template of a k-ary unbiased black-box algorithm for optimizing f. Algorithm 2: Metropolis algorithm for maximizing f Algorithm 3: Sig-c GA for maximizing f |
| Open Source Code | No | The paper does not provide any explicit statements or links for open-source code for the described methodology. |
| Open Datasets | No | The paper defines a synthetic problem called the Deceiving Leading Blocks (DLB) function within the text. It is not an external, publicly available dataset with a concrete access method (link, DOI, etc.). |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits) as it works with a synthetic function (DLB) rather than an external dataset that would typically be split into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions algorithms like (1+1) EA, UMDA, Metropolis algorithm, and sig-c GA, but does not provide specific software names with version numbers or library dependencies. |
| Experiment Setup | Yes | We used the standard mutation rate p = 1/n for the (1 + 1) EA, population sizes µ = 3n ln n and λ = 12µ for the UMDA (as in [Doerr and Krejca, 2020b]), and temperature parameter α = 3 (greater than 2 + 1 as suggested in Theorem 4) for the Metropolis algorithm. For the sig-c GA, we took ϵ = 2.5... |