Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization
Authors: Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yue-Jiao Gong
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Neur ELA achieves consistently superior performance when integrated into different and even unseen Meta BBO tasks and can be efficiently fine-tuned for further performance boost. This advancement marks a pivotal step in making Meta BBO algorithms more autonomous and broadly applicable. |
| Researcher Affiliation | Academia | 1South China University of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudo code of training Neur ELA |
| Open Source Code | Yes | The source code of Neur ELA can be accessed at https://github.com/GMC-DRL/Neur-ELA. |
| Open Datasets | Yes | Then we set the associated problem set Dk for these Meta BBO algorithms as the BBOB testsuites in COCO Hansen et al. (2021), which includes a variety of optimization problems with diverse landscape properties. We have to note that the selected algorithms cover diverse Meta BBO scenarios such as auto-configuration for control parameters and auto-selection for evolutionary operators, hence ensuring the generalization of our Neur ELA. |
| Dataset Splits | Yes | Specifically we visualize these 24 problems under 2D setting, and then select 12 representative problems into train set. |
| Hardware Specification | Yes | The experiments are run on a computation node of a Slurm CPU cluster, with two Intel Sapphire Rapids 6458Q CPUs and 256 GB memories. |
| Software Dependencies | No | Our codebase can be accessed at https://anonymous.4open.science/r/ Neur-ELA-303C. In Table 3 we listed several open-sourced assets used in our work and their corresponding licenses. Table 3: Used open-sourced tools and their licenses. Used scenario Asset License Top-level optimizer Py Pop7 Duan et al. (2022) GPL-3.0 license Meta BBO algorithms implementation Low-level train-test workflow Meta Box Ma et al. (2023) BSD-3-Clause license Parallel processing Ray Moritz et al. (2018) Apache-2.0 license ELA feature calculation pflacco Kerschke & Trautmann (2019b) MIT license. The provided text does not include specific version numbers for the listed software. |
| Experiment Setup | Yes | For the settings about the neural network, we set the hidden dimension h = 16 for the neural network modules in the landscape analyser Λθ. With a single-head attention in Attn, Λθ possess a total number of 3296 learnable parameters. We adopt Fast CMA-ES Li et al. (2018) as ES for its searching efficiency and robust optimization performance. During the training, we employ a population of N = 10 Λθs within a generation and optimize the population for max Gen = 50 generations. For each generation, we parallel the N K = 30 meta-training pipelines across 30 independent CPU cores using Ray Moritz et al. (2018). For each low-level optimization process, we set the maximum function evaluations as 20000. The experiments are run on a computation node of a Slurm CPU cluster, with two Intel Sapphire Rapids 6458Q CPUs and 256 GB memories. Due to the limitation of the space, we present more technical details such as the train-test split {Dtrain, Dtest}, the control parameters of Fast CMAES and the use of open-sourced software in Appendix A.2 A.5. |