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New FMQA framework uses stage-dependent encoding for black-box optimization

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种用于因子分解机二次优化退火（FMQA）的阶段相关框架，以改进黑盒优化。这种新方法允许在代理学习阶段和伊辛机解搜索阶段使用不同的整数-二进制编码，解决了传统FMQA在整个过程中使用单一编码的局限性。所提出的框架在Rastrigin函数上进行了评估，结果表明独热编码对于学习阶段的性能至关重要，始终产生较低的残差误差。在更精细的离散化级别下，通过切换到域壁编码进行解搜索阶段，观察到了进一步的改进。 AI

影响 这项研究可能导致更有效的机器学习复杂问题优化技术。

排序理由 该项目是一篇研究论文，详细介绍了一个新的优化框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New FMQA framework uses stage-dependent encoding for black-box optimization

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shu Tanaka ·

    分解机黑盒优化中的阶段依赖整数-二进制编码

    Black-box optimization (BBO) deals with problems where objective functions lack explicit analytical forms and are expensive to evaluate. Factorization machine with quadratic-optimization annealing (FMQA) constructs a surrogate model using a factorization machine (FM) and optimize…

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