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 87 articles , 1 standard article )

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  1. de Souza, Marcelo; Ritt, Marcus; López-Ibáñez, Manuel: Capping methods for the automatic configuration of optimization algorithms (2022)
  2. Hall, George T.; Oliveto, Pietro S.; Sudholt, Dirk: On the impact of the performance metric on efficient algorithm configuration (2022)
  3. Ansótegui, Carlos; Ojeda, Jesús; Pacheco, Antonio; Pon, Josep; Salvia, Josep M.; Torres, Eduard: OptiLog: a framework for SAT-based systems (2021)
  4. Ansótegui, Carlos; Pon, Josep; Sellmann, Meinolf; Tierney, Kevin: PyDGGA: distributed GGA for automatic configuration (2021)
  5. Bertsimas, Dimitris; Stellato, Bartolomeo: The voice of optimization (2021)
  6. Corazza, Marco; di Tollo, Giacomo; Fasano, Giovanni; Pesenti, Raffaele: A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (2021)
  7. de Oliveira, Sabrina M.; Bezerra, Leonardo C. T.; Stützle, Thomas; Dorigo, Marco; Wanner, Elizabeth F.; de Souza, Sérgio R.: A computational study on ant colony optimization for the traveling salesman problem with dynamic demands (2021)
  8. Diaz, Juan Esteban; López-Ibáñez, Manuel: Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms (2021)
  9. Grimme, Christian; Kerschke, Pascal; Aspar, Pelin; Trautmann, Heike; Preuss, Mike; Deutz, André H.; Wang, Hao; Emmerich, Michael: Peeking beyond peaks: challenges and research potentials of continuous multimodal multi-objective optimization (2021)
  10. Hodashinsky, I. A.: Methods for improving the efficiency of swarm optimization algorithms. A survey (2021)
  11. Manthey, Norbert: The \textscMergeSatsolver (2021)
  12. Nof, Yair; Strichman, Ofer: Real-time solving of computationally hard problems using optimal algorithm portfolios (2021)
  13. Ottervanger, Gilles; Baratchi, Mitra; Hoos, Holger H.: MultiETSC: automated machine learning for early time series classification (2021)
  14. Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer: DACBench: A Benchmark Library for Dynamic Algorithm Configuration (2021) arXiv
  15. Vallati, Mauro; Chrpa, Lukáš; McCluskey, Thomas Leo; Hutter, Frank: On the importance of domain model configuration for automated planning engines (2021)
  16. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  17. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  18. Bowly, Simon; Smith-Miles, Kate; Baatar, Davaatseren; Mittelmann, Hans: Generation techniques for linear programming instances with controllable properties (2020)
  19. Jarvis, Padraigh; Arbelaez, Alejandro: Cooperative parallel SAT local search with path relinking (2020)
  20. Moreno, Alfredo; Munari, Pedro; Alem, Douglas: Decomposition-based algorithms for the crew scheduling and routing problem in road restoration (2020)

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