Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SYMBOL: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Authors: Jiacheng Chen, Zeyuan Ma, Hongshu Guo, Yining Ma, Jie Zhang, Yue-Jiao Gong
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments reveal that the optimizers generated by SYMBOL not only surpass the state-of-the-art BBO and Meta BBO baselines, but also exhibit exceptional zero-shot generalization abilities across entirely unseen tasks with different problem dimensions, population sizes, and optimization horizons. Furthermore, we conduct in-depth analyses of our SYMBOL framework and the optimization rules that it generates, underscoring its desirable flexibility and interpretability. |
| Researcher Affiliation | Academia | Jiacheng Chen1, , Zeyuan Ma1, , Hongshu Guo1, Yining Ma2, Jie Zhang2, Yue-Jiao Gong1, 1 South China University of Technology 2 Nanyang Technological University EMAIL, EMAIL , EMAIL |
| Pseudocode | Yes | Algorithm 2 illustrates the pseudo code for the training process of three strategies of SYMBOL. |
| Open Source Code | Yes | We release the implementation python codes at https://github.com/GMC-DRL/Symbol, where we show how to train SYMBOL with different strategies, and how to generalize it to unseen problems. |
| Open Datasets | Yes | Our training dataset is synthesized based on the well-known IEEE CEC Numerical Optimization Competition (Mohamed et al., 2021) benchmark, which contains ten challenging synthetic BBO problems (f1 f10). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for validation. |
| Hardware Specification | Yes | All experiments are run on a machine with Intel i9-10980XE CPU, RTX 3090 GPU and 32GB RAM. |
| Software Dependencies | No | The paper mentions 'implementation python codes' but does not specify version numbers for Python or any specific libraries/dependencies used. |
| Experiment Setup | Yes | The tunable parameter λ for SYMBOL-S is set to 1. We simultaneously sample a batch of N = 32 problems from problem distribution D for training. The pre-defined maximum learning steps for PPO is 5 104. The learning rate α is 10 3. The number of generations (T) for lower-level optimization is 500. |