# MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization

arXiv: https://arxiv.org/abs/2505.17745 · HTML: https://mlsanity.com/paper/2505.17745/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 81 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 30 of 42)

- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Tianshou: a Highly Modularized Deep Reinforcement Learning Library](https://mlsanity.com/paper/2107.14171/) (2021)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch](https://mlsanity.com/paper/2410.13213/) (2024)
- [LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics](https://mlsanity.com/paper/2405.20132/) (2024)
- [HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML](https://mlsanity.com/paper/2106.06257/) (2021)
- [LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation](https://mlsanity.com/paper/2403.01131/) (2024)
- [Discovering Evolution Strategies via Meta-Black-Box Optimization](https://mlsanity.com/paper/2211.11260/) (2022)
- [Deep Reinforcement Learning Based Parameter Control in Differential Evolution](https://mlsanity.com/paper/1905.08006/) (2019)
- [Multi-agent Dynamic Algorithm Configuration](https://mlsanity.com/paper/2210.06835/) (2022)
- [MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning](https://mlsanity.com/paper/2310.08252/) (2023)
- [Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization](https://mlsanity.com/paper/2304.03995/) (2023)
- [IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics](https://mlsanity.com/paper/1810.05281/) (2018)
- [Learning adaptive differential evolution algorithm from optimization experiences by policy gradient](https://mlsanity.com/paper/2102.03572/) (2021)
- [Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution](https://mlsanity.com/paper/2403.02131/) (2024)
- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning](https://mlsanity.com/paper/2402.02355/) (2024)
- [Meta-Learning for Black-box Optimization](https://mlsanity.com/paper/1907.06901/) (2019)
- [EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation](https://mlsanity.com/paper/2301.12457/) (2023)
- [Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning](https://mlsanity.com/paper/2404.08239/) (2024)
- [B2Opt: Learning to Optimize Black-box Optimization with Little Budget](https://mlsanity.com/paper/2304.11787/) (2023)
- [Pretrained Optimization Model for Zero-Shot Black Box Optimization](https://mlsanity.com/paper/2405.03728/) (2024)
- [Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning](https://mlsanity.com/paper/2503.18061/) (2025)
- [Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results](https://mlsanity.com/paper/1706.03470/) (2017)
- [Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization](https://mlsanity.com/paper/2408.10672/) (2024)
- [Evolution Transformer: In-Context Evolutionary Optimization](https://mlsanity.com/paper/2403.02985/) (2024)
- [MetaDE: Evolving Differential Evolution by Differential Evolution](https://mlsanity.com/paper/2502.10470/) (2025)
- [Meta-Black-Box-Optimization through Offline Q-function Learning](https://mlsanity.com/paper/2505.02010/) (2025)

## Built on by (most cited first, 9 of 9)

- [Task-free Adaptive Meta Black-box Optimization](https://mlsanity.com/paper/2601.21475/) (2026)
- [Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking](https://mlsanity.com/paper/2511.15199/) (2025)
- [A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization](https://mlsanity.com/paper/2604.01241/) (2026)
- [Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA](https://mlsanity.com/paper/2606.00862/) (2026)
- [READY: Reward Discovery for Meta-Black-Box Optimization](https://mlsanity.com/paper/2601.21847/) (2026)
- [SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2601.22131/) (2026)
- [NeurGO: Learning to Generate Elite Candidates for Meta-Black-Box Expensive Optimization](https://mlsanity.com/paper/2607.23408/) (2026)
- [Reinforcement learning to choose optimizers](https://mlsanity.com/paper/2609.01811/) (2026)
- [Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero](https://mlsanity.com/paper/2609.35328/) (2026)

## Abstract (arXiv)

Meta-Black-Box Optimization (MetaBBO) streamlines the automation of optimization algorithm design through meta-learning. It typically employs a bi-level structure: the meta-level policy undergoes meta-training to reduce the manual effort required in developing algorithms for low-level optimization tasks. The original MetaBox (2023) provided the first open-source framework for reinforcement learning-based single-objective MetaBBO. However, its relatively narrow scope no longer keep pace with the swift advancement in this field. In this paper, we introduce MetaBox-v2 (https://github.com/MetaEvo/MetaBox) as a milestone upgrade with four novel features: 1) a unified architecture supporting RL, evolutionary, and gradient-based approaches, by which we reproduce 23 up-to-date baselines; 2) efficient parallelization schemes, which reduce the training/testing time by 10-40x; 3) a comprehensive benchmark suite of 18 synthetic/realistic tasks (1900+ instances) spanning single-objective, multi-objective, multi-model, and multi-task optimization scenarios; 4) plentiful and extensible interfaces for custom analysis/visualization and integrating to external optimization tools/benchmarks. To show the utility of MetaBox-v2, we carry out a systematic case study that evaluates the built-in baselines in terms of the optimization performance, generalization ability and learning efficiency. Valuable insights are concluded from thorough and detailed analysis for practitioners and those new to the field.