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..
Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise
Authors: Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle, Sebastian Will
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we conduct the first mathematical runtime analysis of a simple multi-objective evolutionary algorithm (MOEA) on a classic benchmark in the presence of noise in the objective function. |
| Researcher Affiliation | Academia | 1 Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France 2Laboratoire d Informatique (LIX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France |
| Pseudocode | Yes | Algorithm 1: SEMO without reevaluation; Algorithm 2: SEMO with reevaluation; Algorithm 3: Elimination function elim. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper focuses on theoretical runtime analysis of the SEMO on the ONEMINMAX benchmark, which is a problem definition rather than a dataset with concrete access information (e.g., URL, DOI, or repository). |
| Dataset Splits | No | The paper is a theoretical runtime analysis and does not involve empirical experiments with dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is a theoretical work focusing on mathematical runtime analysis and does not describe any experimental hardware specifications (e.g., CPU, GPU, memory). |
| Software Dependencies | No | The paper is a theoretical work and does not describe any specific software dependencies or versions required to replicate an experimental setup. It focuses on the algorithms themselves rather than their implementation environment. |
| Experiment Setup | No | The paper is a theoretical runtime analysis and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |