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English(EN) MSC-CMA-ES: Structure-Aware Restarts for CMA-ES via Cyclic Nearest-Better Basin Discovery

新的MSC-CMA-ES方法通过结构感知重启增强了多模态搜索

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为MSC-CMA-ES的新优化策略，旨在提高CMA-ES算法在多模态搜索场景中的性能。该方法通过将搜索空间划分为吸引盆并在此类盆中进行重启播种，引入了结构感知重启。在各种基准套件上的评估表明，MSC-CMA-ES在组合函数上表现出色，显示出比其他算法高得多的覆盖率，尽管它在基本函数上显示了景观发现与深度目标覆盖之间的权衡。 AI

排序理由 该集群包含一篇详细介绍新算法及其在基准套件上评估的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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

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新的MSC-CMA-ES方法通过结构感知重启增强了多模态搜索

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该集群包含一篇详细介绍新算法及其在基准套件上评估的研究论文。[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) · Dimitar Pilev ·

    MSC-CMA-ES：通过循环最近更优盆地发现实现结构感知的CMA-ES重启

    CMA-ES is, per run, a local optimizer; multimodal search relies on restart strategies such as IPOP and BIPOP, which draw every restart uniformly and reuse no information from previous evaluations. Multi-Start Clustering CMA-ES (MSC-CMA-ES) makes restarts structure-aware: in alter…

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