Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise
Authors: Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle, Sebastian Will
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |