Pretrained Optimization Model for Zero-Shot Black Box Optimization
Authors: Xiaobin Li, Kai Wu, yujian li, Xiaoyu Zhang, Handing Wang, Jing Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation on the BBOB benchmark and two robot control tasks demonstrates that POM outperforms state-of-the-art black-box optimization methods, especially for high-dimensional tasks. Fine-tuning POM with a small number of samples and budget yields significant performance improvements. Moreover, POM demonstrates robust generalization across diverse task distributions, dimensions, population sizes, and optimization horizons. |
| Researcher Affiliation | Academia | Xiaobin Li Xidian University 22171214784@stu.xidian.edu.cn Kai Wu Xidian University kwu@xidian.edu.cn Yujian Betterrest Li Xidian University bebetterest@outlook.com Xiaoyu Zhang Xidian University xiaoyuzhang@xidian.edu.cn Handing Wang Xidian University hdwang@xidian.edu.cn Jing Liu Xidian University neouma@mail.xidian.edu.cn |
| Pseudocode | Yes | Algorithm 1 Meta GBT Algorithm 2 Driving POM to Solve Problem |
| Open Source Code | Yes | For code implementation, see https://github.com/ninja-wm/POM/. |
| Open Datasets | Yes | We evaluate the generalization ability of POM across 24 BBOB functions with dimensions d = 30 and d = 100, where optimal solutions are located at 0. Figure 2 presents the critical difference diagram comparing all algorithms (refer to Appendix Tables 4 and 6, and Figures 11, 12 and 13 for detailed results). POM significantly outperforms all methods, showcasing its efficacy across varying dimensions. Despite being trained solely on TF1-TF4 with d = 10, POM excels in higher dimensions (d = {30, 100, 500}), with its performance advantage becoming more pronounced with increasing dimensionality. Particularly on complex problems F21-F24, where global structure is weak, POM lags behind LSHADE but surpasses other methods, attributed to its adaptability through fine-tuning. Tur BO [56] is the Bayesian optimization algorithm with the best performance on BBOB [57]. Under little budget conditions, the performance of POM outperforms that of Tur BO in most cases (see Appendix G for details). |
| Dataset Splits | No | The paper does not explicitly mention validation dataset splits or specific validation procedures in terms of percentages or counts for the main experiments, beyond stating that POM is trained on a set of training functions (TS) and then evaluated on benchmark problems. |
| Hardware Specification | Yes | All experiments are performed on a device with Ge Force RTX 3090 24G GPU, Intel Xeon Gold 6126 CPU and 64G RAM. |
| Software Dependencies | No | The paper mentions software packages like "Geatpy [60]", "cmaes2", and "pyade3" used for baselines, but it does not specify their exact version numbers. It mentions "Geatpy: The genetic and evolutionary algorithm toolbox with high performance in python, 2020" which gives a year but not a precise version. |
| Experiment Setup | Yes | POM is trained on TS with T = 100, n = 100, and d = 10. Detailed parameters for all compared methods are provided in Appendix E. Please refer to Appendix D for the reasons for choosing these algorithms. |