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)

Paper ID: h28b22e3694208770
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,698 | 28.08%
DOI: 10.14778/3797919.3797920

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061066921
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
461	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00017829982
560	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00016403151
1,199	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011563985
1,250	DB-BERT: A Database Tuning Tool that "Reads the Manual"	2022	SIGMOD	0.00011339256
1,279	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011226878
1,515	DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems	2021	VLDB	0.00010417728
1,734	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.7545773e-05
2,210	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	8.8257742e-05
2,231	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	8.7982985e-05
2,250	QueryFormer: A Tree Transformer Model for Query Plan Representation	2022	VLDB	8.7533306e-05
2,690	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB	8.1258173e-05
2,719	TPC-DS, Taking Decision Support Benchmarking to the Next Level	2002	SIGMOD	8.0967665e-05
3,487	LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans	2023	VLDB	7.263041e-05
3,978	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	6.8807882e-05
5,241	FASTgres: Making Learned Query Optimizer Hinting Effective	2023	VLDB	6.2154384e-05
6,586	Can Large Language Models Be Query Optimizer for Relational Databases?	2026	SIGMOD	5.7430662e-05
9,431	MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud	2024	VLDB	5.1783663e-05

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