LIO: A lightweight and interpretable query optimizer based on an evolutionary forest
Summary: LIO is a lightweight learned query optimizer using genetic programming to select interpretable random-forest features, balancing accuracy, cost, and interpretability. Pruning and hint-guided refinement improve plans while reducing forest complexity and runtime. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Chen Ye (Hangzhou Dianzi University)
- 2. Shujie Ma (Hangzhou Dianzi University)
- 3. Guojun Dai (Hangzhou Dianzi University)
- 4. Hengtong Zhang (Harbin Engineering University)
BibTeX Citation
@article{ye_vldb26,
title = {{LIO: A lightweight and interpretable query optimizer based on an evolutionary forest}},
author = {Ye, Chen and Ma, Shujie and Dai, Guojun and Zhang, Hengtong},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {6},
pages = {1088--1100},
doi = {10.14778/3797919.3797920},
url = {https://doi.org/10.14778/3797919.3797920},
year = {2026}
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