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 32 articles , 1 standard article )

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  1. Ansótegui, Carlos; Gabàs, Joel; Malitsky, Yuri; Sellmann, Meinolf: MaxSAT by improved instance-specific algorithm configuration (2016)
  2. 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)
  3. KhudaBukhsh, Ashiqur R.; Xu, Lin; Hoos, Holger H.; Leyton-Brown, Kevin: SATenstein: automatically building local search SAT solvers from components (2016)
  4. Balint, Adrian; Belov, Anton; Järvisalo, Matti; Sinz, Carsten: Overview and analysis of the SAT challenge 2012 solver competition (2015)
  5. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)
  6. Kühlwein, Daniel; Urban, Josef: MaLeS: A framework for automatic tuning of automated theorem provers (2015)
  7. Núñez, Sergio; Borrajo, Daniel; Linares López, Carlos: Automatic construction of optimal static sequential portfolios for AI planning and beyond (2015)
  8. López-Ibáñez, Manuel; Stützle, Thomas: Automatically improving the anytime behaviour of optimisation algorithms (2014)
  9. 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)
  10. Stojadinović, Mirko; Marić, Filip: meSAT: multiple encodings of CSP to SAT (2014)
  11. Dubec, Patrik; Plucar, Jan; Rapant, Lukáš: Case study of evolutionary process visualization using complex networks (2013)
  12. 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)
  13. Bellio, Ruggero; Di Gaspero, Luca; Schaerf, Andrea: Design and statistical analysis of a hybrid local search algorithm for course timetabling (2012)
  14. 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)
  15. Domshlak, C.; Karpas, E.; Markovitch, S.: Online speedup learning for optimal planning (2012)
  16. Grøtli, Esten Ingar; Johansen, Tor Arne: Path planning for uavs under communication constraints using SPLAT! and MILP (2012)
  17. Lierler, Yuliya; Schüller, Peter: Parsing combinatory categorial grammar via planning in answer set programming (2012)
  18. Smith-Miles, Kate; Lopes, Leo: Measuring instance difficulty for combinatorial optimization problems (2012)
  19. Gebser, Martin; Kaminski, Roland; Kaufmann, Benjamin; Schaub, Torsten; Schneider, Marius Thomas; Ziller, Stefan: A portfolio solver for answer set programming: preliminary report (2011)
  20. Masegosa, Antonio D.; Pelta, David A.; González, Juan R.: Solving multiple instances at once: the role of search and adaptation (2011)

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