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新的FrugalEvo框架通过成本优化LLM驱动的程序演化

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出FrugalEvo，一个新颖的框架，旨在通过考虑计算成本来优化程序演化。该方法利用一个更强大、更昂贵的LLM来制定策略，并利用一个更便宜的LLM来实现和优化生成的代码。FrugalEvo旨在在固定预算内最大化性能提升，引入预算感知曲线下面积（BA-AUC）指标来衡量质量与成本的关系。在数学、系统和算法优化任务的评估中，FrugalEvo匹配或超越了现有的最先进方法，尤其是在圆 packing 任务中取得了新的最先进成果，与多智能体系统相比成本显著降低。 AI

影响 引入了一种成本感知的LLM驱动程序演化方法，有望降低复杂优化任务的计算费用。

排序理由 这是一篇详细介绍程序演化新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的FrugalEvo框架通过成本优化LLM驱动的程序演化

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这是一篇详细介绍程序演化新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hui Chen, Xuan Qi, James Xu Zhao, Zhaopeng Feng, Shilong Liu, Kuang Xu, Pang Wei Koh, Bryan Hooi ·

    FrugalEvo：迈向成本感知型 LLM 指导的程序演化

    arXiv:2610.03675v1 Announce Type: cross Abstract: LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Bryan Hooi ·

    FrugalEvo：面向成本感知的LLM引导程序演化

    LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a fixed number of iterations. We argue that practic…

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