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English(EN) Semantics-Guided Automatic Tensorization for Multiobjective Evolutionary Algorithms: A Multi-Agent Framework

新框架EvoCoCo优化进化算法以进行张量计算

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了EvoCoCo，一个多智能体框架，旨在为现代张量计算平台自动重构多目标进化算法（MOEAs）。该框架旨在增强GPU等硬件上的计算可扩展性和性能，而无需改变MOEAs的核心优化机制。实验表明，与直接翻译相比，EvoCoCo实现了更高的迁移可靠性，并显著加速了MOEA的实现，测得的速度提升幅度从22.6倍到80.2倍不等，具体取决于缩放因子。 AI

影响 这项研究通过优化计算框架，可能带来更高效的AI模型训练和推理。

排序理由 该集群包含一篇详细介绍新优化算法框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架EvoCoCo优化进化算法以进行张量计算

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该集群包含一篇详细介绍新优化算法框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Ran Cheng ·

    面向多目标进化算法的语义引导自动张量化：一个多智能体框架

    Multiobjective evolutionary algorithms (MOEAs) naturally expose population-level parallelism, but many mature implementations encode their computation in sequential program structures designed for central processing units. Exploiting modern tensor computing platforms therefore re…

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