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 121 articles )

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  1. Swan, Jerry; Adriaensen, Steven; Brownlee, Alexander E. I.; Hammond, Kevin; Johnson, Colin G.; Kheiri, Ahmed; Krawiec, Faustyna; Merelo, J. J.; Minku, Leandro L.; Özcan, Ender; Pappa, Gisele L.; García-Sánchez, Pablo; Sörensen, Kenneth; Voß, Stefan; Wagner, Markus; White, David R.: Metaheuristics “In the large” (2022)
  2. Zhou, Qing; Hao, Jin-Kao; Wu, Qinghua: A hybrid evolutionary search for the generalized quadratic multiple knapsack problem (2022)
  3. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  4. Andrade, Carlos E.; Toso, Rodrigo F.; Gonçalves, José F.; Resende, Mauricio G. C.: The multi-parent biased random-key genetic algorithm with implicit path-relinking and its real-world applications (2021)
  5. Averbakh, Igor; Pereira, Jordi: Tree optimization based heuristics and metaheuristics in network construction problems (2021)
  6. Bertsimas, Dimitris; Stellato, Bartolomeo: The voice of optimization (2021)
  7. Blum, Christian; Ochoa, Gabriela: A comparative analysis of two matheuristics by means of merged local optima networks (2021)
  8. Corazza, Marco; di Tollo, Giacomo; Fasano, Giovanni; Pesenti, Raffaele: A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (2021)
  9. Diaz, Juan Esteban; López-Ibáñez, Manuel: Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms (2021)
  10. Fink, Andreas; Gerhards, Patrick: Negotiation mechanisms for the multi-agent multi-mode resource investment problem (2021)
  11. Gliesch, Alex; Ritt, Marcus: A hybrid heuristic for the maximum dispersion problem (2021)
  12. Jesus, Alexandre D.; Paquete, Luís; Liefooghe, Arnaud: A model of anytime algorithm performance for bi-objective optimization (2021)
  13. Limbeck, Jan; Bisdom, Kevin; Lanz, Fabian; Park, Timothy; Barbaro, Eduardo; Bourne, Stephen; Kiraly, Franz; Bierman, Stijn; Harris, Chris; Nevenzeel, Keimpe; den Bezemer, Taco; van Elk, Jan: Using machine learning for model benchmarking and forecasting of depletion-induced seismicity in the Groningen gas field (2021)
  14. Mathlouthi, Ines; Gendreau, Michel; Potvin, Jean-Yves: A metaheuristic based on tabu search for solving a technician routing and scheduling problem (2021)
  15. Máximo, Vinícius R.; Nascimento, Mariá C. V.: A hybrid adaptive iterated local search with diversification control to the capacitated vehicle routing problem (2021)
  16. Melo, Rafael A.; Queiroz, Michell F.; Ribeiro, Celso C.: Compact formulations and an iterated local search-based matheuristic for the minimum weighted feedback vertex set problem (2021)
  17. Nof, Yair; Strichman, Ofer: Real-time solving of computationally hard problems using optimal algorithm portfolios (2021)
  18. Pan, Binbin; Zhang, Zhenzhen; Lim, Andrew: Multi-trip time-dependent vehicle routing problem with time windows (2021)
  19. Pan, Binbin; Zhang, Zhenzhen; Lim, Andrew: A hybrid algorithm for time-dependent vehicle routing problem with time windows (2021)
  20. Rodríguez, Alma; Camarena, Octavio; Cuevas, Erik; Aranguren, Itzel; Valdivia-G, Arturo; Morales-Castañeda, Bernardo; Zaldívar, Daniel; Pérez-Cisneros, Marco: Group-based synchronous-asynchronous grey wolf optimizer (2021)

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