Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
Authors: Weijie Zheng, Benjamin Doerr
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |