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 61 articles , 1 standard article )

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  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. Kochemazov, Stepan; Zaikin, Oleg: ALIAS: a modular tool for finding backdoors for SAT (2018)
  5. Woo, Young-Bin; Kim, Byung Soo: Matheuristic approaches for parallel machine scheduling problem with time-dependent deterioration and multiple rate-modifying activities (2018)
  6. Adamo, Tommaso; Ghiani, Gianpaolo; Grieco, Antonio; Guerriero, Emanuela; Manni, Emanuele: MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration (2017)
  7. Barbosa, Eduardo Batista de Moraes; Senne, Edson Luiz França: Improving the fine-tuning of metaheuristics: an approach combining design of experiments and racing algorithms (2017)
  8. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  9. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  10. Karapetyan, Daniel; Punnen, Abraham P.; Parkes, Andrew J.: Markov chain methods for the bipartite Boolean quadratic programming problem (2017)
  11. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  12. Luo, Chuan; Cai, Shaowei; Su, Kaile; Huang, Wenxuan: CCEHC: an efficient local search algorithm for weighted partial maximum satisfiability (2017)
  13. Mısır, Mustafa; Sebag, Michèle: Alors: an algorithm recommender system (2017)
  14. Ostrowski, Krzysztof; Karbowska-Chilinska, Joanna; Koszelew, Jolanta; Zabielski, Pawel: Evolution-inspired local improvement algorithm solving orienteering problem (2017)
  15. Pérez Cáceres, Leslie; Stützle, Thomas: Exploring variable neighborhood search for automatic algorithm configuration (2017)
  16. Rahimian, Erfan; Akartunalı, Kerem; Levine, John: A hybrid integer programming and variable neighbourhood search algorithm to solve nurse rostering problems (2017)
  17. 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)
  18. Ansótegui, Carlos; Gabàs, Joel; Malitsky, Yuri; Sellmann, Meinolf: MaxSAT by improved instance-specific algorithm configuration (2016)
  19. 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)
  20. Cai, Shaowei; Luo, Chuan; Lin, Jinkun; Su, Kaile: New local search methods for partial MaxSAT (2016)

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