Paramils: an automatic algorithm configuration framework. The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.

References in zbMATH (referenced in 54 articles , 1 standard article )

Showing results 1 to 20 of 54.
Sorted by year (citations)

1 2 3 next

  1. Cerutti, Federico; Vallati, Mauro; Giacomin, Massimiliano: On the impact of configuration on abstract argumentation automated reasoning (2018)
  2. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  3. Gnad, Daniel; Hoffmann, Jörg: Star-topology decoupled state space search (2018)
  4. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  5. Karapetyan, Daniel; Punnen, Abraham P.; Parkes, Andrew J.: Markov chain methods for the bipartite Boolean quadratic programming problem (2017)
  6. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  7. Luo, Chuan; Cai, Shaowei; Su, Kaile; Huang, Wenxuan: CCEHC: an efficient local search algorithm for weighted partial maximum satisfiability (2017)
  8. Mısır, Mustafa; Sebag, Michèle: Alors: an algorithm recommender system (2017)
  9. Ostrowski, Krzysztof; Karbowska-Chilinska, Joanna; Koszelew, Jolanta; Zabielski, Pawel: Evolution-inspired local improvement algorithm solving orienteering problem (2017)
  10. Pérez Cáceres, Leslie; Stützle, Thomas: Exploring variable neighborhood search for automatic algorithm configuration (2017)
  11. Rahimian, Erfan; Akartunalı, Kerem; Levine, John: A hybrid integer programming and variable neighbourhood search algorithm to solve nurse rostering problems (2017)
  12. Soria-Alcaraz, Jorge A.; Ochoa, Gabriela; Sotelo-Figeroa, Marco A.; Burke, Edmund K.: A methodology for determining an effective subset of heuristics in selection hyper-heuristics (2017)
  13. Ansótegui, Carlos; Gabàs, Joel; Malitsky, Yuri; Sellmann, Meinolf: MaxSAT by improved instance-specific algorithm configuration (2016)
  14. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  15. Cai, Shaowei; Luo, Chuan; Lin, Jinkun; Su, Kaile: New local search methods for partial MaxSAT (2016)
  16. Inala, Jeevana Priya; Singh, Rohit; Solar-Lezama, Armando: Synthesis of domain specific CNF encoders for bit-vector solvers (2016)
  17. Jan Jakubuv, Josef Urban: BliStrTune: Hierarchical Invention of Theorem Proving Strategies (2016) arXiv
  18. KhudaBukhsh, Ashiqur R.; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: SATenstein: automatically building local search SAT solvers from components (2016)
  19. Lombardi, Michele; Gualandi, Stefano: A Lagrangian propagator for artificial neural networks in constraint programming (2016)
  20. Manthey, Norbert; Lindauer, Marius: Spybug: automated bug detection in the configuration space of SAT solvers (2016)

1 2 3 next