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 79 articles )

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  1. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  2. Araya, Ignacio; Moyano, Mauricio; Sanchez, Cristobal: A beam search algorithm for the biobjective container loading problem (2020)
  3. da Silva, André Renato Villela; Ochi, Luiz Satoru; da Silva Barros, Bruno José; Pinheiro, Rian Gabriel S.: Efficient approaches for the flooding problem on graphs (2020)
  4. Djukanovic, Marko; Raidl, Günther R.; Blum, Christian: Anytime algorithms for the longest common palindromic subsequence problem (2020)
  5. Dokka, Trivikram; Goerigk, Marc; Roy, Rahul: Mixed uncertainty sets for robust combinatorial optimization (2020)
  6. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  7. Eng, KaiLun; Muhammed, Abdullah; Mohamed, Mohamad Afendee; Hasan, Sazlinah: A hybrid heuristic of variable neighbourhood descent and great deluge algorithm for efficient task scheduling in grid computing (2020)
  8. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  9. Hao Wang, Diederick Vermetten, Carola Doerr, Thomas Bäck: IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic (2020) arXiv
  10. Li, Mingjie; Hao, Jin-Kao; Wu, Qinghua: General swap-based multiple neighborhood adaptive search for the maximum balanced biclique problem (2020)
  11. Lobo, Fernando G.; Bazargani, Mosab; Burke, Edmund K.: A cutoff time strategy based on the coupon collector’s problem (2020)
  12. Pinto, Bruno Q.; Ribeiro, Celso C.; Rosseti, Isabel; Noronha, Thiago F.: A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model (2020)
  13. Ritt, Marcus; Pereira, Jordi: Heuristic and exact algorithms for minimum-weight non-spanning arborescences (2020)
  14. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  15. Soares, Leonardo Cabral R.; Carvalho, Marco Antonio M.: Biased random-key genetic algorithm for scheduling identical parallel machines with tooling constraints (2020)
  16. Soriano, Adria; Vidal, Thibaut; Gansterer, Margaretha; Doerner, Karl: The vehicle routing problem with arrival time diversification on a multigraph (2020)
  17. Toutouh, Jamal; Rossit, Diego; Nesmachnow, Sergio: Soft computing methods for multiobjective location of garbage accumulation points in smart cities (2020)
  18. van Bulck, David; Goossens, Dries: Handling fairness issues in time-relaxed tournaments with availability constraints (2020)
  19. Zhou, Qing; Benlic, Una; Wu, Qinghua: An opposition-based memetic algorithm for the maximum quasi-clique problem (2020)
  20. Elgers, Niels; Dang, Nguyen; De Causmaecker, Patrick: A metaheuristic approach to compute pure Nash equilibria (2019)

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