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 Analysis of the SMS-EMOA for Many-Objective Optimization
Authors: Weijie Zheng, Benjamin Doerr
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper conducts the first rigorous runtime analysis of the SMS-EMOA for many-objective optimization. To this aim, we first propose a many-objective counterpart, the m-objective m OJZJ problem, of the bi-objective OJZJ benchmark, which is the first many-objective multimodal benchmark used in a mathematical runtime analysis. We prove that SMS-EMOA computes the full Pareto front of this benchmark in an expected number of O(M 2nk) iterations |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, China 2 Laboratoire d Informatique (LIX), Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France |
| Pseudocode | Yes | Algorithm 1: SMS-EMOA |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described in this paper. |
| Open Datasets | No | The paper defines and analyzes theoretical benchmarks (e.g., m OJZJ, ONEMINMAX, LOTZ) rather than using empirical datasets. These benchmarks are mathematical problem definitions, not publicly available data in the traditional sense. |
| Dataset Splits | No | As a theoretical runtime analysis paper, it does not involve empirical experiments with training, validation, or test data splits. |
| Hardware Specification | No | As a theoretical runtime analysis paper, it does not describe specific hardware used for running experiments. |
| Software Dependencies | No | As a theoretical runtime analysis paper, it does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | As a theoretical runtime analysis paper, it does not describe an experimental setup with hyperparameters or system-level training settings. |