arXiv (All)
open access
Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization
Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models replace most expensive evaluations and critically influence the final optimization results. In recent years, tabular foundation models have advanced rap
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Instance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture#986789
- Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization#1051506
- Training Fair Tabular Foundation Models#458298
- Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap#200481
- Architecture Alignment With Sparse Priors in Tabular Foundation Models#1132084
Record · ID 1011826
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive