HyFlex: a benchmark framework for cross-domain heuristic search. This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer: DACBench: A Benchmark Library for Dynamic Algorithm Configuration (2021) arXiv
  2. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  3. Soria-Alcaraz, Jorge A.; Ochoa, Gabriela; Sotelo-Figeroa, Marco A.; Burke, Edmund K.: A methodology for determining an effective subset of heuristics in selection hyper-heuristics (2017)
  4. Kheiri, Ahmed; Özcan, Ender: An iterated multi-stage selection hyper-heuristic (2016)
  5. Meignan, David; Schwarze, Silvia; Voß, Stefan: Improving local-search metaheuristics through look-ahead policies (2016) ioport
  6. Pillay, Nelishia: A review of hyper-heuristics for educational timetabling (2016)
  7. Soria-Alcaraz, Jorge A.; Ochoa, Gabriela; Swan, Jerry; Carpio, Martin; Puga, Hector; Burke, Edmund K.: Effective learning hyper-heuristics for the course timetabling problem (2014)
  8. Hadwan, Mohammed; Ayob, Masri; Sabar, Nasser R.; Qu, Roug: A harmony search algorithm for nurse rostering problems (2013) ioport
  9. Kalender, Murat; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: A greedy gradient-simulated annealing selection hyper-heuristic (2013) ioport
  10. Mısır, Mustafa; Verbeeck, Katja; De Causmaecker, Patrick; Vanden Berghe, Greet: A new hyper-heuristic as a general problem solver: an implementation in hyflex (2013) ioport
  11. Ochoa, Gabriela; Hyde, Matthew; Curtois, Tim; Vazquez-Rodriguez, Jose A.; Walker, James; Gendreau, Michel; Kendall, Graham; McCollum, Barry; Parkes, Andrew J.; Petrovic, Sanja; Burke, Edmund K.: HyFlex: a benchmark framework for cross-domain heuristic search (2012)
  12. Ochoa, Gabriela; Walker, James; Hyde, Matthew; Curtois, Tim: Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework (2012) ioport