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 38 articles , 1 standard article )

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  1. Mısır, Mustafa; Sebag, Michèle: Alors: an algorithm recommender system (2017)
  2. Ansótegui, Carlos; Gabàs, Joel; Malitsky, Yuri; Sellmann, Meinolf: MaxSAT by improved instance-specific algorithm configuration (2016)
  3. 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)
  4. Cai, Shaowei; Luo, Chuan; Lin, Jinkun; Su, Kaile: New local search methods for partial MaxSAT (2016)
  5. Jan Jakubuv, Josef Urban: BliStrTune: Hierarchical Invention of Theorem Proving Strategies (2016) arXiv
  6. KhudaBukhsh, Ashiqur R.; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: SATenstein: automatically building local search SAT solvers from components (2016)
  7. Lombardi, Michele; Gualandi, Stefano: A Lagrangian propagator for artificial neural networks in constraint programming (2016)
  8. Balint, Adrian; Belov, Anton; Järvisalo, Matti; Sinz, Carsten: Overview and analysis of the SAT challenge 2012 solver competition (2015) ioport
  9. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)
  10. Kühlwein, Daniel; Urban, Josef: MaLeS: a framework for automatic tuning of automated theorem provers (2015)
  11. Núñez, Sergio; Borrajo, Daniel; Linares López, Carlos: Automatic construction of optimal static sequential portfolios for AI planning and beyond (2015)
  12. López-Ibáñez, Manuel; Stützle, Thomas: Automatically improving the anytime behaviour of optimisation algorithms (2014)
  13. Soria-Alcaraz, Jorge A.; Ochoa, Gabriela; Swan, Jerry; Carpio, Martin; Puga, Hector; Burke, Edmund K.: Effective learning hyper-heuristics for the course timetabling problem (2014)
  14. Stojadinović, Mirko; Marić, Filip: meSAT: multiple encodings of CSP to SAT (2014)
  15. Dubec, Patrik; Plucar, Jan; Rapant, Lukáš: Case study of evolutionary process visualization using complex networks (2013) ioport
  16. Humeau, J.; Liefooghe, A.; Talbi, E.-G.; Verel, S.: ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms (2013)
  17. Kristiansen, Simon; Sørensen, Matias; Herold, Michael B.; Stidsen, Thomas R.: The consultation timetabling problem at Danish high schools (2013)
  18. Liao, Tianjun; de Oca, Marco A.Montes; Stützle, Thomas: Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set (2013) ioport
  19. Bellio, Ruggero; Di Gaspero, Luca; Schaerf, Andrea: Design and statistical analysis of a hybrid local search algorithm for course timetabling (2012) ioport
  20. Ceschia, Sara; Di Gaspero, Luca; Schaerf, Andrea: Design, engineering, and experimental analysis of a simulated annealing approach to the post-enrolment course timetabling problem (2012) ioport

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