{"ID":2829387,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12809","arxiv_id":"2512.12809","title":"OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization","abstract":"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.","short_abstract":"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-Programme...","url_abs":"https://arxiv.org/abs/2512.12809","url_pdf":"https://arxiv.org/pdf/2512.12809v1","authors":"[\"Junbo Jacob Lian\",\"Mingyang Yu\",\"Kaichen Ouyang\",\"Shengwei Fu\",\"Rui Zhong\",\"Yujun Zhang\",\"Jun Zhang\",\"Huiling Chen\"]","published":"2025-12-14T19:16:49Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\"]","methods":"[\"Graph Neural Network\",\"LoRA\"]","has_code":false}