# Warmstarting of Model-based Algorithm Configuration

arXiv: https://arxiv.org/abs/1709.04636 · HTML: https://mlsanity.com/paper/1709.04636/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 17 of 17)

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- [Tetra-AML: Automatic Machine Learning via Tensor Networks](https://mlsanity.com/paper/2303.16214/) (2023)
- [Towards a General Framework for Predicting and Explaining the Hardness of Graph-based Combinatorial Optimization Problems using Machine Learning and Association Rule Mining](https://mlsanity.com/paper/2512.20915/) (2025)
- [An Empirical Study on Ensemble-Based Transfer Learning Bayesian Optimisation with Mixed Variable Types](https://mlsanity.com/paper/2601.15640/) (2026)
- [A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting](https://mlsanity.com/paper/2602.01445/) (2026)
- [Transfer Learning in Bayesian Optimization for Aircraft Design](https://mlsanity.com/paper/2603.28999/) (2026)

## Abstract (arXiv)

The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a very flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.