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New methods use geometry and CNNs for algorithm selection

作者 PulseAugur 编辑部 · [2 个来源] ·

Two new research papers explore novel methods for selecting the best algorithm for continuous black-box optimization tasks. One paper, GeoPAS, uses geometric probing to create 2D slices of the objective landscape, encoding these slices to represent problem instances and then selecting solvers based on a composite score. The other paper, using CNNs, visualizes probed landscapes as contour plots, feeding these images into a convolutional neural network to predict solver performance and guide selection. Both approaches aim to significantly outperform relying on a single best solver, demonstrating improved efficiency and robustness across various optimization scenarios. AI

影响 These novel approaches to algorithm selection could lead to more efficient and robust optimization processes in various scientific and engineering fields.

排序理由 Two academic papers published on arXiv detailing new methods for algorithm selection in optimization.

在 arXiv cs.LG 阅读 →

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New methods use geometry and CNNs for algorithm selection

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jiabao Brad Wang, Xiang Shi, Yiliang Yuan, Mustafa Misir ·

    GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization

    arXiv:2604.09095v3 Announce Type: replace Abstract: Automated algorithm selection for continuous black-box optimization depends on representing problem information under limited probing and selecting solvers under heavy-tailed performance distributions. This paper proposes a geom…

  2. arXiv cs.LG TIER_1 English(EN) · Mustafa Misir ·

    Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization

    The present paper introduces a new representation-driven approach to per-instance algorithm selection, applied to black-box optimization, for automatically choosing the most promising solver from a fixed portfolio. Prior work in continuous optimization largely relies on numerical…

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