# OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization

arXiv: https://arxiv.org/abs/2512.12809 · HTML: https://mlsanity.com/paper/2512.12809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 14 Dec 2025.

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

- [Automated Dynamic Algorithm Configuration](https://mlsanity.com/paper/2205.13881/) (2022)
- [Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning](https://mlsanity.com/paper/1711.08921/) (2017)
- [DACBench: A Benchmark Library for Dynamic Algorithm Configuration](https://mlsanity.com/paper/2105.08541/) (2021)
- [Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization](https://mlsanity.com/paper/2408.10672/) (2024)
- [Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization](https://mlsanity.com/paper/2407.13513/) (2024)
- [Meta-Black-Box Optimization with Bi-Space Landscape Analysis and Dual-Control Mechanism for SAEA](https://mlsanity.com/paper/2511.15551/) (2025)
- [A Landscape-Aware Differential Evolution for Multimodal Optimization Problems](https://mlsanity.com/paper/2408.02340/) (2024)
- [Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification](https://mlsanity.com/paper/2409.01446/) (2024)

## Abstract (arXiv)

Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabulary of search operators and learn this operator program separately for each problem instance. We instantiate this idea in Operator-Programmed Algorithms (OPAL), a landscape-aware framework for continuous black-box optimization that uses a small design budget with a standard differential evolution baseline to probe the landscape, builds a k-nearest neighbor graph over sampled points, and encodes this trajectory with a graph neural network. A meta-learner then maps the resulting representation to a phase-wise schedule of exploration, restart, and local search operators. On the CEC~2017 test suite, a single meta-trained OPAL policy is statistically competitive with state-of-the-art adaptive differential evolution variants and achieves significant improvements over simpler baselines under nonparametric tests. Ablation studies on CEC~2017 justify the choices for the design phase, the trajectory graph, and the operator-program representation, while the meta-components add only modest wall-clock overhead. Overall, the results indicate that operator-programmed, landscape-aware per-instance design is a practical way forward beyond ad hoc metaphor-based algorithms in black-box optimization.