Paramils

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 46 articles , 1 standard article )

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  1. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  2. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  3. Luo, Chuan; Cai, Shaowei; Su, Kaile; Huang, Wenxuan: CCEHC: an efficient local search algorithm for weighted partial maximum satisfiability (2017)
  4. Mısır, Mustafa; Sebag, Michèle: Alors: an algorithm recommender system (2017)
  5. Ostrowski, Krzysztof; Karbowska-Chilinska, Joanna; Koszelew, Jolanta; Zabielski, Pawel: Evolution-inspired local improvement algorithm solving orienteering problem (2017)
  6. Ansótegui, Carlos; Gabàs, Joel; Malitsky, Yuri; Sellmann, Meinolf: MaxSAT by improved instance-specific algorithm configuration (2016)
  7. 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)
  8. Cai, Shaowei; Luo, Chuan; Lin, Jinkun; Su, Kaile: New local search methods for partial MaxSAT (2016)
  9. Inala, Jeevana Priya; Singh, Rohit; Solar-Lezama, Armando: Synthesis of domain specific CNF encoders for bit-vector solvers (2016)
  10. Jan Jakubuv, Josef Urban: BliStrTune: Hierarchical Invention of Theorem Proving Strategies (2016) arXiv
  11. KhudaBukhsh, Ashiqur R.; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: SATenstein: automatically building local search SAT solvers from components (2016)
  12. Lombardi, Michele; Gualandi, Stefano: A Lagrangian propagator for artificial neural networks in constraint programming (2016)
  13. Manthey, Norbert; Lindauer, Marius: Spybug: automated bug detection in the configuration space of SAT solvers (2016)
  14. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Why CP portfolio solvers are (under)utilized? issues and challenges (2015)
  15. Balint, Adrian; Belov, Anton; Järvisalo, Matti; Sinz, Carsten: Overview and analysis of the SAT challenge 2012 solver competition (2015) ioport
  16. Falkner, Stefan; Lindauer, Marius; Hutter, Frank: SpySMAC: automated configuration and performance analysis of SAT solvers (2015)
  17. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)
  18. Kühlwein, Daniel; Urban, Josef: MaLeS: a framework for automatic tuning of automated theorem provers (2015)
  19. Núñez, Sergio; Borrajo, Daniel; Linares López, Carlos: Automatic construction of optimal static sequential portfolios for AI planning and beyond (2015)
  20. López-Ibáñez, Manuel; Stützle, Thomas: Automatically improving the anytime behaviour of optimisation algorithms (2014)

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