Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits
Authors: Tian Huang, Shengbo Wang, Ke Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 48 benchmark test problems, including the RNA inverse design and protein structure prediction, fully demonstrate the effectiveness of our proposed approach. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering , University of Electronic Science and Technology of China 2 Department of Computer Science, University of Exeter |
| Pseudocode | Yes | Algorithm 1 D-PBEMO structure |
| Open Source Code | Yes | The code of our algorithms and peer algorithms are available at https://github.com/COLA-Laboratory/EMOC/. |
| Open Datasets | Yes | Our experiments considers 33 synthetic test instances including ZDT1 to ZDT4 and ZDT6 [72] (m = 2), DTLZ1 to DTLZ6 [18] where m = {3, 5, 8, 10}, and WFG1, WFG3, WFG5, and WFG7 [30] (m = 3). ... In addition, we also consider two scientific discovery problems including 10 two-objective RNA inverse design tasks [55, 70] and 5 four-objective protein structure prediction (PSP) task [69]. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits (e.g., percentages or sample counts) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, memory amounts, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | In our experiment, the MFE is calculate by Vienna RNA3 package. [Footnote 3] https://github.com/Vienna RNA/Vienna RNA. No specific version numbers for software dependencies are provided beyond this mention. |
| Experiment Setup | Yes | The probability and distribution of index for SBX: pc = 1.0 and ηc = 20; The mutation probability and distribution of index for polynomial mutation operator: pm = 1 m and ηm = 20; The population size for different problems can be referenced in Table A2; The maximum number of generation G can be referenced in Table A3; For I-MOEA/D-PLVF and I-NSGA2/LTR, the number of incumbent candidate presented to decison maker (DM) for consultation: µ = 10; The budget for dueling bandits algorithm T is set as 100. |