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English(EN) NeurGO: Learning to Generate Elite Candidates for Meta-Black-Box Expensive Optimization

NeurGO框架为昂贵的优化问题生成精英候选者

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了NeurGO，一种用于昂贵黑盒优化问题的新型生成框架。该方法直接从历史种群数据合成精英候选解决方案，无需评估大量劣质选项。通过采用基于注意力的编码器和质量多样性损失，NeurGO旨在捕捉搜索趋势并保持解决方案的多样性。在CEC 2008和COCO BBOB等标准测试套件上的实验表明，NeurGO在有限的评估预算内实现了卓越的优化性能和更快的收敛速度。 AI

影响 该框架可以提高计算资源有限的科学和工程应用中的效率。

排序理由 该集群包含一篇详细介绍优化问题新算法框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

NeurGO框架为昂贵的优化问题生成精英候选者

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该集群包含一篇详细介绍优化问题新算法框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jintao He, Huixiang Zhen, Wenyin Gong ·

    NeurGO：学习生成元黑盒昂贵优化的精英候选者

    arXiv:2607.23408v1 Announce Type: new Abstract: Expensive black-box optimization is ubiquitous in science and engineering, where function evaluations are costly and the evaluation budget is limited. Traditional evolutionary algorithms and Meta-BlackBox Optimization (MetaBBO) appr…

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