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 22 articles )

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  1. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  2. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  3. Pérez Cáceres, Leslie; Stützle, Thomas: Exploring variable neighborhood search for automatic algorithm configuration (2017)
  4. Vallée, Sven; Oulamara, Ammar; Cherif-Khettaf, Wahiba Ramdane: Maximizing the number of served requests in an online shared transport system by solving a dynamic DARP (2017)
  5. Bischl, Bernd; Kühn, Tobias; Szepannek, Gero: On class imbalance correction for classification algorithms in credit scoring (2016)
  6. Blum, Christian; Pinacho, Pedro; López-Ibáñez, Manuel; Lozano, José A.: Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization (2016)
  7. Calvete, Herminia I.; Galé, Carmen; Iranzo, José A.: MEALS: a multiobjective evolutionary algorithm with local search for solving the bi-objective ring star problem (2016)
  8. Chaves, A.A.; Lorena, L.A.N.; Senne, E.L.F.; Resende, M.G.C.: Hybrid method with CS and BRKGA applied to the minimization of tool switches problem (2016)
  9. Chen, Yuning; Hao, Jin-Kao; Glover, Fred: A hybrid metaheuristic approach for the capacitated arc routing problem (2016)
  10. Chivilikhin, D.S.; Ulyantsev, V.I.; Shalyto, A.A.: Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulas (2016)
  11. Cortinhal, Maria João; Mourão, Maria C^andida; Nunes, Ana Catarina: Local search heuristics for sectoring routing in a household waste collection context (2016)
  12. DellAmico, Mauro; Iori, Manuel; Novellani, Stefano; Stützle, Thomas: A destroy and repair algorithm for the bike sharing rebalancing problem (2016)
  13. François, Véronique; Arda, Yasemin; Crama, Yves; Laporte, Gilbert: Large neighborhood search for multi-trip vehicle routing (2016)
  14. Martin, Simon; Ouelhadj, Djamila; Beullens, Patrick; Ozcan, Ender; Juan, Angel A.; Burke, Edmund K.: A multi-agent based cooperative approach to scheduling and routing (2016)
  15. Barbosa, Eduardo Batista de Moraes; Senne, Edson Luiz França; Silva, Messias Borges: Improving the performance of metaheuristics: an approach combining response surface methodology and racing algorithms (2015)
  16. Falkner, Stefan; Lindauer, Marius; Hutter, Frank: SpySMAC: automated configuration and performance analysis of SAT solvers (2015)
  17. Caraffini, Fabio; Neri, Ferrante; Picinali, Lorenzo: An analysis on separability for memetic computing automatic design (2014) ioport
  18. Lacroix, Benjamin; Molina, Daniel; Herrera, Francisco: Region based memetic algorithm for real-parameter optimisation (2014)
  19. Liao, Tianjun; Stützle, Thomas; Montes de Oca, Marco A.; Dorigo, Marco: A unified ant colony optimization algorithm for continuous optimization (2014)
  20. López-Ibáñez, Manuel; Stützle, Thomas: Automatically improving the anytime behaviour of optimisation algorithms (2014)

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