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Machine learning enhances black-box optimization solvers, survey finds

By PulseAugur Editorial · [1 sources] ·

A new survey paper explores the application of machine learning (ML) and reinforcement learning (RL) techniques to enhance black-box optimization (BBO) solvers. The paper details how ML can improve BBO by providing better surrogates, adaptive updates, meta-learning, and generative models. It covers various algorithms like mlrMBO, ZO-AdaMM, and Q-Mamba, and discusses benchmark efforts such as the NeurIPS 2020 BBO Challenge and the MetaBox framework. A numerical study within the paper suggests that ML-based candidate screening can boost the performance of direct search and differential evolution methods, though the benefits are solver-dependent. AI

IMPACT This research could lead to more efficient and robust optimization methods across various scientific and engineering domains.

RANK_REASON The cluster contains a survey paper detailing research advancements in machine learning algorithms for improving black-box optimization solvers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning enhances black-box optimization solvers, survey finds

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The cluster contains a survey paper detailing research advancements in machine learning algorithms for improving black-box optimization solvers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Morteza Kimiaei, Vyacheslav Kungurtsev ·

    Machine Learning Algorithms for Improving Black Box Optimization Solvers

    arXiv:2509.25592v2 Announce Type: replace Abstract: Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods---line search, direct search, and model-based…

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