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 geometric probing framework that represents each problem instance by randomly sampled multi-scale two-dimensional slices of the objective landscape. The slices are encoded with validity-mask-aware visual pooling and aggregated into an instance representation. Solver selection is then performed by a logarithmic composite sc
The continuous evolution of AI and machine learning pushes for more sophisticated and automated optimization techniques, driven by increasing computational capabilities and data complexity.
This development offers a potential breakthrough in automating the notoriously difficult task of algorithm selection in black-box optimization, crucial for scientific discovery, engineering, and AI system design.
The ability to geometrically probe and represent complex problem landscapes allows for more intelligent and automated selection of optimization algorithms, enhancing efficiency and robustness in various applications.
- · AI/ML researchers
- · Optimization software developers
- · Industries relying on complex black-box optimization (e.g., drug discovery, mate
- · Cloud computing providers offering optimization services
- · Manual algorithm selection experts
- · Inefficient brute-force optimization methods
Black-box optimization becomes more accessible and efficient, accelerating research and development in computationally intensive fields.
The improved optimization capabilities lead to faster progress in AI model training, autonomous systems, and advanced engineering design.
Automated discovery of novel materials, drugs, or complex system configurations could significantly accelerate technological and scientific breakthroughs, impacting global competitiveness.
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Read at arXiv cs.LG