# PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization

arXiv: https://arxiv.org/abs/2503.22722 · HTML: https://mlsanity.com/paper/2503.22722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Diffusion Models for Black-Box Optimization](https://mlsanity.com/paper/2306.07180/) (2023)
- [Discovering Evolution Strategies via Meta-Black-Box Optimization](https://mlsanity.com/paper/2211.11260/) (2022)
- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning](https://mlsanity.com/paper/2310.08252/) (2023)
- [Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution](https://mlsanity.com/paper/2403.02131/) (2024)
- [EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation](https://mlsanity.com/paper/2301.12457/) (2023)
- [MetaDE: Evolving Differential Evolution by Differential Evolution](https://mlsanity.com/paper/2502.10470/) (2025)
- [Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit](https://mlsanity.com/paper/1612.07603/) (2016)

## Built on by (most cited first, 1 of 1)

- [MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization](https://mlsanity.com/paper/2505.17745/) (2025)

## Abstract (arXiv)

The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques. Meta-Black-Box Optimization (MetaBBO), which involves refining the optimization algorithms themselves via meta-learning, has emerged as a promising approach. Recognizing the limitations in existing platforms, we presents PlatMetaX, a novel MATLAB platform for MetaBBO with reinforcement learning. PlatMetaX integrates the strengths of MetaBox and PlatEMO, offering a comprehensive framework for developing, evaluating, and comparing optimization algorithms. The platform is designed to handle a wide range of optimization problems, from single-objective to multi-objective, and is equipped with a rich set of baseline algorithms and evaluation metrics. We demonstrate the utility of PlatMetaX through extensive experiments and provide insights into its design and implementation. PlatMetaX is available at: hrefhttps://github.com/Yxxx616/PlatMetaXhttps://github.com/Yxxx616/PlatMetaX.