Warmstarting of Model-Based Algorithm Configuration


  • Marius Lindauer University of Freiburg
  • Frank Hutter University of Freiburg




Algorithm Configuration, Parameter Tuning, Empirical Algorithmics


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 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.




How to Cite

Lindauer, M., & Hutter, F. (2018). Warmstarting of Model-Based Algorithm Configuration. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11532



AAAI Technical Track: Heuristic Search and Optimization