irace

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 142 articles )

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  1. Horn, Matthias; Raidl, Günther; Blum, Christian: Job sequencing with one common and multiple secondary resources: an A*/beam search based anytime algorithm (2019)
  2. Ji, Bin; Yuan, Xiaohui; Yuan, Yanbin; Lei, Xiaohui; Fernando, Tyrone; Iu, Herbert H. C.: Exact and heuristic methods for optimizing lock-quay system in inland waterway (2019)
  3. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A population algorithm based on randomized tabu thresholding for the multi-commodity pickup-and-delivery traveling salesman problem (2019)
  4. Lu, Yongliang; Hao, Jin-Kao; Wu, Qinghua: Hybrid evolutionary search for the traveling repairman problem with profits (2019)
  5. Maniezzo, Vittorio; Boschetti, Marco A.; Carbonaro, Antonella; Marzolla, Moreno; Strappaveccia, Francesco: Client-side computational optimization (2019)
  6. Mischek, Florian; Musliu, Nysret: Integer programming model extensions for a multi-stage nurse rostering problem (2019)
  7. Pagnozzi, Federico; Stützle, Thomas: Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems (2019)
  8. Strub, O.; Trautmann, N.: A two-stage approach to the UCITS-constrained index-tracking problem (2019)
  9. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  10. Zhou, Qing; Benlic, Una; Wu, Qinghua; Hao, Jin-Kao: Heuristic search to the capacitated clustering problem (2019)
  11. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  12. Camarena, Octavio; Cuevas, Erik; Pérez-Cisneros, Marco; Fausto, Fernando; González, Adrián; Valdivia, Arturo; Rodriguez-Tello, Eduardo: LS-II: an improved locust search algorithm for solving optimization problems (2018)
  13. Chen, Yuning; Hao, Jin-Kao: Two phased hybrid local search for the periodic capacitated arc routing problem (2018)
  14. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  15. El Yafrani, Mohamed; Ahiod, Belaïd: Efficiently solving the traveling thief problem using hill climbing and simulated annealing (2018)
  16. Franzin, Alberto; Pérez Cáceres, Leslie; Stützle, Thomas: Effect of transformations of numerical parameters in automatic algorithm configuration (2018)
  17. 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)
  18. Furini, Fabio; Malaguti, Enrico; Santini, Alberto: An exact algorithm for the partition coloring problem (2018)
  19. Kiefer, Alexander; Schilde, Michael; Doerner, Karl F.: Scheduling of maintenance work of a large-scale tramway network (2018)
  20. Lu, Yongliang; Benlic, Una; Wu, Qinghua: A memetic algorithm for the orienteering problem with mandatory visits and exclusionary constraints (2018)