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. Lu, Yongliang; Benlic, Una; Wu, Qinghua: Multi-restart iterative search for the pickup and delivery traveling salesman problem with FIFO loading (2018)
  2. Melchiori, Anna; Sgalambro, Antonino: A matheuristic approach for the quickest multicommodity (k)-splittable flow problem (2018)
  3. Paquay, Célia; Limbourg, Sabine; Schyns, Michaël: A tailored two-phase constructive heuristic for the three-dimensional multiple bin size bin packing problem with transportation constraints (2018)
  4. Pereira, Jordi; Ritt, Marcus; Vásquez, Óscar C.: A memetic algorithm for the cost-oriented robotic assembly line balancing problem (2018)
  5. Pessoa, Luciana S.; Andrade, Carlos E.: Heuristics for a flowshop scheduling problem with stepwise job objective function (2018)
  6. Pinacho Davidson, Pedro; Blum, Christian; Lozano, Jose A.: The weighted independent domination problem: integer linear programming models and metaheuristic approaches (2018)
  7. Pinto, Bruno Q.; Ribeiro, Celso C.; Rosseti, Isabel; Plastino, Alexandre: A biased random-key genetic algorithm for the maximum quasi-clique problem (2018)
  8. Sengupta, Raunak; Saha, Sriparna: Reference point based archived many objective simulated annealing (2018)
  9. Zubaran, Tadeu K.; Ritt, Marcus: An effective heuristic algorithm for the partial shop scheduling problem (2018)
  10. Adamo, Tommaso; Ghiani, Gianpaolo; Grieco, Antonio; Guerriero, Emanuela; Manni, Emanuele: MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration (2017)
  11. Burgiel, Heidi; El-Hashash, Mahmoud: Open questions on tantrix graphs (2017)
  12. Chen, Yuning; Hao, Jin-Kao: An iterated “hyperplane exploration” approach for the quadratic knapsack problem (2017)
  13. Drake, John H.; Swan, Jerry; Neumann, Geoff; Özcan, Ender: Sparse, continuous policy representations for uniform online bin packing via regression of interpolants (2017)
  14. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  15. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  16. Lizárraga, Evelia; Blesa, María J.; Blum, Christian: Construct, merge, solve and adapt versus large neighborhood search for solving the multi-dimensional knapsack problem: which one works better when? (2017)
  17. Ma, Fuda; Hao, Jin-Kao; Wang, Yang: An effective iterated tabu search for the maximum bisection problem (2017)
  18. Pérez Cáceres, Leslie; Stützle, Thomas: Exploring variable neighborhood search for automatic algorithm configuration (2017)
  19. Silva Paiva, Gustavo; Carvalho, Marco Antonio M.: Improved heuristic algorithms for the job sequencing and tool switching problem (2017)
  20. 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)