Published on April 23, 2026
In traditional algorithm selection, engineers often relied on extensive domain knowledge to craft features that determine the best algorithm for a given problem. This reliance on hand-crafted instance features has constrained innovation and efficiency in diverse problem domains. However, a new approach known as ZeroFolio is challenging this norm.
ZeroFolio employs a feature-free methodology text embeddings to eliminate the need for domain expertise. It transforms raw instance files into embeddings and applies a weighted k-nearest neighbors algorithm to select the optimal solution. This streamlined three-step process allows for broader application across various fields such as SAT and graph problems.
In testing, ZeroFolio achieved impressive results across 11 ASlib scenarios spanning seven different domains. It consistently outperformed conventional methods, including a random forest relying on hand-crafted features, in 10 of the scenarios and in all 11 when using a two-seed voting technique. The findings underscore the potential of embedding models to create competitive advantages without prior training.
The implications of this method are significant. dependence on domain knowledge, ZeroFolio makes advanced algorithms more accessible to practitioners unfamiliar with specific areas. This democratization of algorithm selection could accelerate advancements in AI and problem-solving across various domains, fostering innovation and collaboration.
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