Solving High-Dimensional Multi-Objective Optimization Problems with Low Effective Dimensions
Authors: Hong Qian, Yang Yu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that Re MO is effective for optimizing the high-dimensional MO functions with low effective dimensions, and is even effective for the high-dimensional MO functions where all dimensions are effective but most only have a small and bounded effect on the function value. |
| Researcher Affiliation | Academia | Hong Qian, Yang Yu National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023, China {qianh,yuy}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Multi-Objective Optimization via Random Embedding (Re MO) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | We first verify the effectiveness of Re MO empirically on three high-dimensional bi-objective (i.e., m = 2) optimization testing functions ZDT10, ZDT20 and ZDT30 with low M-effective dimensions. They are constructed based on the first three bi-objective functions ZDT1, ZDT2 and ZDT3 introduced in (Zitzler, Deb, and Thiele 2000). |
| Dataset Splits | No | The paper uses synthetic test functions (ZDT series) for which standard train/validation/test splits are not applicable in the same way as for empirical datasets. No explicit details on data partitioning or validation splits are provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions several algorithms like NSGA-II and MOEA/D, stating 'The implementations of them are both by their authors,' but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We set the function evaluation budget n = 0.3D = 3000 and the upper bound of M-effective dimension ϑ = 50 > ϑe = 30. We set the function evaluation budget n = 0.3D = 3000 and set the reference point as (1, 4) to calculate the hyper-volume indicator (the larger the better). |