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 73 articles , 1 standard article )

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  1. Diaz, Juan Esteban; López-Ibáñez, Manuel: Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms (2021)
  2. Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer: DACBench: A Benchmark Library for Dynamic Algorithm Configuration (2021) arXiv
  3. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  4. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  5. Bowly, Simon; Smith-Miles, Kate; Baatar, Davaatseren; Mittelmann, Hans: Generation techniques for linear programming instances with controllable properties (2020)
  6. Moreno, Alfredo; Munari, Pedro; Alem, Douglas: Decomposition-based algorithms for the crew scheduling and routing problem in road restoration (2020)
  7. Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank: Pitfalls and best practices in algorithm configuration (2019)
  8. Franzin, Alberto; Stützle, Thomas: Revisiting simulated annealing: a component-based analysis (2019)
  9. Liu, Jianfeng; Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Tuning BARON using derivative-free optimization algorithms (2019)
  10. Cerutti, Federico; Vallati, Mauro; Giacomin, Massimiliano: On the impact of configuration on abstract argumentation automated reasoning (2018)
  11. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  12. Franzin, Alberto; Pérez Cáceres, Leslie; Stützle, Thomas: Effect of transformations of numerical parameters in automatic algorithm configuration (2018)
  13. Gnad, Daniel; Hoffmann, Jörg: Star-topology decoupled state space search (2018)
  14. Kochemazov, Stepan; Zaikin, Oleg: ALIAS: a modular tool for finding backdoors for SAT (2018)
  15. Sengupta, Raunak; Saha, Sriparna: Reference point based archived many objective simulated annealing (2018)
  16. Woo, Young-Bin; Kim, Byung Soo: Matheuristic approaches for parallel machine scheduling problem with time-dependent deterioration and multiple rate-modifying activities (2018)
  17. Adamo, Tommaso; Ghiani, Gianpaolo; Grieco, Antonio; Guerriero, Emanuela; Manni, Emanuele: MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration (2017)
  18. 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)
  19. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  20. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)

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