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New Adaptive Multi Mode optimizer routes budgets for continuous black-box problems

Researchers have developed Adaptive Multi Mode (AMM), a novel system for optimizing continuous black-box problems. AMM dynamically routes budgets among various optimization engines, including quasi-Newton, trust-region, and evolution strategies, by using online probes to assess problem landscapes. In tests on the BBOB benchmark, AMM demonstrated superior performance across a wide range of function-dimension-budget cells, outperforming competitors and its own control configurations. The system has been released as an open-source Python package. AI

IMPACT This new optimization routing system could improve efficiency in various machine learning tasks that rely on continuous black-box optimization.

RANK_REASON The cluster contains a research paper detailing a new optimization method.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Adaptive Multi Mode optimizer routes budgets for continuous black-box problems

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The cluster contains a research paper detailing a new optimization method.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Benjamin Jacob Bodner ·

    Adaptive Multi Mode: Budget-Aware Routing of Optimizers By Online Landscape Probes

    arXiv:2611.06879v1 Announce Type: new Abstract: The best classical optimizer for a continuous black-box problem depends on its budget and landscape. Existing routers decide from the dimension and budget, past improvement or features fitted to one benchmark, and few test an optimi…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Benjamin Jacob Bodner ·

    Adaptive Multi Mode: Budget-Aware Routing of Optimizers By Online Landscape Probes

    The best classical optimizer for a continuous black-box problem depends on its budget and landscape. Existing routers decide from the dimension and budget, past improvement or features fitted to one benchmark, and few test an optimizer's implicit assumptions online. AMM (Adaptive…