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. Turkeš, Renata; Sörensen, Kenneth; Hvattum, Lars Magnus: Meta-analysis of metaheuristics: quantifying the effect of adaptiveness in adaptive large neighborhood search (2021)
  2. Wang, Yiyuan; Pan, Shiwei; Al-Shihabi, Sameh; Zhou, Junping; Yang, Nan; Yin, Minghao: An improved configuration checking-based algorithm for the unicost set covering problem (2021)
  3. Weiner, Jake; Ernst, Andreas T.; Li, Xiaodong; Sun, Yuan; Deb, Kalyanmoy: Solving the maximum edge disjoint path problem using a modified Lagrangian particle swarm optimisation hybrid (2021)
  4. Wickert, Toni I.; Kummer Neto, Alberto F.; Boniatti, Márcio M.; Buriol, Luciana S.: An integer programming approach for the physician rostering problem (2021)
  5. Alfaro-Fernández, Pedro; Ruiz, Rubén; Pagnozzi, Federico; Stützle, Thomas: Automatic algorithm design for hybrid flowshop scheduling problems (2020)
  6. Araya, Ignacio; Moyano, Mauricio; Sanchez, Cristobal: A beam search algorithm for the biobjective container loading problem (2020)
  7. Baioletti, Marco; Milani, Alfredo; Santucci, Valentino: Variable neighborhood algebraic differential evolution: an application to the linear ordering problem with cumulative costs (2020)
  8. Bassin, Anton; Buzdalov, Maxim: An experimental study of operator choices in the ((1+(\lambda,\lambda))) genetic algorithm (2020)
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  10. 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)
  11. Djukanovic, Marko; Raidl, Günther R.; Blum, Christian: Anytime algorithms for the longest common palindromic subsequence problem (2020)
  12. Dokka, Trivikram; Goerigk, Marc; Roy, Rahul: Mixed uncertainty sets for robust combinatorial optimization (2020)
  13. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  14. 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)
  15. Felipe Campelo, Lucas Batista, Claus Aranha: The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition (2020) not zbMATH
  16. Frohner, Nikolaus; Neumann, Bernhard; Raidl, Günther R.: A beam search approach to the traveling tournament problem (2020)
  17. Gerhards, Patrick: The multi-mode resource investment problem: a benchmark library and a computational study of lower and upper bounds (2020)
  18. Hao Wang, Diederick Vermetten, Carola Doerr, Thomas Bäck: IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic (2020) arXiv
  19. Lanza-Gutierrez, Jose M.; Caballe, N. C.; Crawford, Broderick; Soto, Ricardo; Gomez-Pulido, Juan A.; Paredes, Fernando: Exploring further advantages in an alternative formulation for the set covering problem (2020)
  20. Leng, Longlong; Zhang, Jingling; Zhang, Chunmiao; Zhao, Yanwei; Wang, Wanliang; Li, Gongfa: Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects (2020)