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English(EN) From Consistency to Collaborative Discovery: MFEA-CoD for Multitask Novelty Search

新的 BEACON 策略增强了昂贵发现任务的新颖性搜索

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员推出了一种受贝叶斯优化启发的、用于新颖性搜索的新策略 BEACON。该方法专为评估成本高昂的场景设计，例如材料科学和分子设计，旨在发现各种系统行为，而非单一的最佳结果。BEACON 使用多输出高斯过程对输入到结果的关系进行建模，并通过评估可能的后验结果与先前观察到的数据的偏差程度来选择新的输入，同时考虑预测不确定性和噪声。 AI

影响 这项研究通过提高探索巨大可能性空间的效率，有可能加速材料科学和分子设计等领域的发现。

排序理由 该集群包含一篇详细介绍新方法的学术论文。[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 3 个来源。 我们如何撰写摘要 →

新的 BEACON 策略增强了昂贵发现任务的新颖性搜索

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该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [3]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Yew-Soon Ong ·

    从一致性到协作发现：用于多任务新颖性搜索的 MFEA-CoD

    Evolutionary multitasking (EMT) has shown strong capability in solving multiple optimization problems simultaneously by exploiting latent inter-task consistency, such as similarities in promising solutions or search directions. However, most existing EMT studies remain focused on…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Yew-Soon Ong ·

    从一致性到协作发现：用于多任务新颖性搜索的 MFEA-CoD

    Evolutionary multitasking (EMT) has shown strong capability in solving multiple optimization problems simultaneously by exploiting latent inter-task consistency, such as similarities in promising solutions or search directions. However, most existing EMT studies remain focused on…

  3. arXiv stat.ML TIER_1 English(EN) · Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson ·

    BEACON：一种受贝叶斯优化启发的有效新颖性搜索策略

    arXiv:2406.03616v5 Announce Type: replace Abstract: Novelty search (NS) aims to uncover diverse system behaviors through simulation or experiment without requiring a pre-specified scalar objective. This capability is especially relevant to modern discovery problems in chemistry, …

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