The irace Package: Iterated Race for Automatic Algorithm Configuration. The irace package implements the iterated racing procedure, which is an extension of the Iterated F-race procedure. Its main purpose is to automatically configure optimization algorithms by finding the most appropriate settings given a set of instances of an optimization problem. It builds upon the race package by Birattari and it is implemented in R. Keywords: automatic configuration, offline tuning, parameter tuning, racing, F-race. Relevant literature: Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Thomas Stützle, and Mauro Birattari. The irace package, Iterated Race for Automatic Algorithm Configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université libre de Bruxelles, Belgium, 2011.

References in zbMATH (referenced in 44 articles )

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  1. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  2. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  3. Diaz, Juan Esteban; Handl, Julia; Xu, Dong-Ling: Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system (2018)
  4. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  5. Friedrich, Christian; Klausnitzer, Armin; Lasch, Rainer: Integrated slicing tree approach for solving the facility layout problem with input and output locations based on contour distance (2018)
  6. Furini, Fabio; Malaguti, Enrico; Santini, Alberto: An exact algorithm for the partition coloring problem (2018)
  7. Kiefer, Alexander; Schilde, Michael; Doerner, Karl F.: Scheduling of maintenance work of a large-scale tramway network (2018)
  8. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A memetic algorithm for the orienteering problem with mandatory visits and exclusionary constraints (2018)
  9. Lu, Yongliang; Benlic, Una; Wu, Qinghua: Multi-restart iterative search for the pickup and delivery traveling salesman problem with FIFO loading (2018)
  10. Melchiori, Anna; Sgalambro, Antonino: A matheuristic approach for the quickest multicommodity $k$-splittable flow problem (2018)
  11. Paquay, Célia; Limbourg, Sabine; Schyns, Michaël: A tailored two-phase constructive heuristic for the three-dimensional multiple bin size bin packing problem with transportation constraints (2018)
  12. Pereira, Jordi; Ritt, Marcus; Vásquez, Óscar C.: A memetic algorithm for the cost-oriented robotic assembly line balancing problem (2018)
  13. Pessoa, Luciana S.; Andrade, Carlos E.: Heuristics for a flowshop scheduling problem with stepwise job objective function (2018)
  14. Pinacho Davidson, Pedro; Blum, Christian; Lozano, Jose A.: The weighted independent domination problem: integer linear programming models and metaheuristic approaches (2018)
  15. Pinto, Bruno Q.; Ribeiro, Celso C.; Rosseti, Isabel; Plastino, Alexandre: A biased random-key genetic algorithm for the maximum quasi-clique problem (2018)
  16. Zubaran, Tadeu K.; Ritt, Marcus: An effective heuristic algorithm for the partial shop scheduling problem (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. Chen, Yuning; Hao, Jin-Kao: An iterated “hyperplane exploration” approach for the quadratic knapsack problem (2017)
  19. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  20. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)

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