A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms
Authors: Chao Bian, Chao Qian, Ke Tang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a general approach to estimating upper bounds on the expected running time of multi-objective EAs (MOEAs), and then apply it to diverse situations, including bi-objective and many-objective optimization as well as exact and approximate analysis. For some known asymptotic bounds, our analysis not only provides their leading constants, but also improves them asymptotically. Moreover, our results provide some theoretical justification for the good empirical performance of MOEAs in solving multi-objective combinatorial problems. |
| Researcher Affiliation | Academia | 1 Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China 2 Shenzhen Key Lab of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China |
| Pseudocode | Yes | Algorithm 1 GSEMO Algorithm |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper focuses on theoretical running time analysis of algorithms on pseudo-Boolean problems (LOTZ, COCZ, m COCZ, WOMM) which are problem definitions rather than empirical datasets with access information. No publicly available or open dataset is used or referenced with access details for training models. |
| Dataset Splits | No | The paper is theoretical and focuses on running time analysis rather than empirical evaluation with datasets. Therefore, there is no mention of training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical, focusing on the running time analysis of algorithms. It does not describe any experimental setup or mention specific hardware specifications used for computation. |
| Software Dependencies | No | The paper is theoretical and analyzes algorithms mathematically. It does not mention any specific software dependencies or versions needed to replicate experimental results. |
| Experiment Setup | No | The paper is theoretical and provides mathematical analyses of algorithm running times. It does not describe an experimental setup with hyperparameter values, training configurations, or system-level training settings. |