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 28 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. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  4. Pinacho Davidson, Pedro; Blum, Christian; Lozano, Jose A.: The weighted independent domination problem: integer linear programming models and metaheuristic approaches (2018)
  5. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  6. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  7. Pérez Cáceres, Leslie; Stützle, Thomas: Exploring variable neighborhood search for automatic algorithm configuration (2017)
  8. 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)
  9. Bischl, Bernd; Kühn, Tobias; Szepannek, Gero: On class imbalance correction for classification algorithms in credit scoring (2016)
  10. Blum, Christian; Pinacho, Pedro; López-Ibáñez, Manuel; Lozano, José A.: Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization (2016)
  11. 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)
  12. 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)
  13. Chen, Yuning; Hao, Jin-Kao; Glover, Fred: A hybrid metaheuristic approach for the capacitated arc routing problem (2016)
  14. 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)
  15. Cortinhal, Maria João; Mourão, Maria C^andida; Nunes, Ana Catarina: Local search heuristics for sectoring routing in a household waste collection context (2016)
  16. DellAmico, Mauro; Iori, Manuel; Novellani, Stefano; Stützle, Thomas: A destroy and repair algorithm for the bike sharing rebalancing problem (2016)
  17. François, Véronique; Arda, Yasemin; Crama, Yves; Laporte, Gilbert: Large neighborhood search for multi-trip vehicle routing (2016)
  18. 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)
  19. 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)
  20. Buzhinsky, I.P.; Kazakov, S.V.; Ulyantsev, V.I.; Tsarev, F.N.; Shalyto, A.A.: Modification of the method of generation of control finite-state machines with continuous actions based on training examples (2015)

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