References

  1. OptaPlanner User Guide. Available at https://docs.optaplanner.org/7.15.0.Final/optaplanner-docs/html_single/index.html.
  2. (2014): IBM CPLEX CP Optimizer. Available at http://www-01.ibm.com/software/commerce/optimization/cplex-cp-optimizer/.
  3. Özgür Akgün, Saad Attieh, Ian P. Gent, Christopher Jefferson, Ian Miguel, Peter Nightingale, András Z. Salamon, Patrick Spracklen & James Wetter (2018): A Framework for Constraint Based Local Search using Essence. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, pp. 1242–1248, doi:10.24963/ijcai.2018/173.
  4. Gustav Björdal, Pierre Flener, Justin Pearson, Peter J. Stuckey & Guido Tack (2018): Declarative Local-Search Neighbourhoods in MiniZinc. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 98–105, doi:10.1109/ICTAI.2018.00025.
  5. Gustav Björdal, Jean-Noël Monette, Pierre Flener & Justin Pearson (2015): A constraint-based local search backend for MiniZinc. Constraints 20(3), pp. 325–345, doi:10.1007/s10601-015-9184-z.
  6. Herman De Beukelaer, Guy F. Davenport, Geert De Meyer & Veerle Fack (2017): JAMES: An object-oriented Java framework for discrete optimization using local search metaheuristics. Software: Practice and Experience 47(6), pp. 921–938, doi:10.1002/spe.2459.
  7. Thomas Elsken, Jan Hendrik Metzen & Frank Hutter (2019): Neural Architecture Search. In: Frank Hutter, Lars Kotthoff & Joaquin Vanschoren: Automated Machine Learning: Methods, Systems, Challenges. Springer International Publishing, Cham, pp. 63–77, doi:10.1007/978-3-030-05318-5_3.
  8. Robert Fourer, David M. Gay & Brian W. Kernighan (1990): A Modeling Language for Mathematical Programming. Management Science 36(5), pp. 519–554, doi:10.1287/mnsc.36.5.519. Available at https://pubsonline.informs.org/doi/abs/10.1287/mnsc.36.5.519.
  9. Alan M. Frisch, Warwick Harvey, Chris Jefferson, Bernadette Martínez-Hernández & Ian Miguel (2008): Essence: A constraint language for specifying combinatorial problems. Constraints 13(3), pp. 268–306, doi:10.1007/s10601-008-9047-y.
  10. Luca Di Gaspero & Andrea Schaerf (2003): EASYLOCAL++: an object-oriented framework for the flexible design of local-search algorithms. Software: Practice and Experience 33(8), pp. 733–765, doi:10.1002/spe.524. Available at https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.524.
  11. Pascal Van Hentenryck & Laurent Michel (2005): Constraint-based local search. MIT Press.
  12. Holger H. Hoos, Marius Thomas Lindauer & Torsten Schaub (2014): claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. TPLP 14(4-5), pp. 569–585, doi:10.1017/S1471068414000210.
  13. Holger H. Hoos & Edward Tsang (2006): Local Search Methods. In: Handbook of Constraint Programming, Foundations of Artificial Intelligence. Elsevier Science Inc., New York, NY, USA, pp. 245–277, doi:10.1016/S1574-6526(06)80009-X.
  14. A. Kaul, S. Maheshwary & V. Pudi (2017): AutoLearn Automated Feature Generation and Selection. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 217–226, doi:10.1109/ICDM.2017.31.
  15. Renaud De Landtsheer, Yoann Guyot, Gustavo Ospina & Christophe Ponsard (2018): Combining Neighborhoods into Local Search Strategies. In: Recent Developments in Metaheuristics, Operations Research/Computer Science Interfaces Series. Springer, Cham, pp. 43–57, doi:10.1007/978-3-319-58253-5_3.
  16. Marco Maratea, Luca Pulina & Francesco Ricca (2014): A multi-engine approach to answer-set programming. TPLP 14(6), pp. 841–868, doi:10.1017/S1471068413000094.
  17. Michael Marte (2017): Yuck is a constraint-based local-search solver with FlatZinc interface. Available at https://github.com/informarte/yuck. Accessed 2019/05/13.
  18. Laurent Michel & Pascal Van Hentenryck (1997): Localizer A modeling language for local search. In: Gert Smolka: Principles and Practice of Constraint Programming-CP97, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 237–251, doi:10.1007/BFb0017443.
  19. Nicholas Nethercote, Peter J. Stuckey, Ralph Becket, Sebastian Brand, Gregory J. Duck & Guido Tack (2007): MiniZinc: Towards a Standard CP Modelling Language. In: Christian Bessière: Principles and Practice of Constraint Programming CP 2007 4741. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 529–543, doi:10.1007/978-3-540-74970-7_38.
  20. Nikolaj van Omme, Laurent Perron & Vincent Furnon (2014): or-tools user's manual. Technical Report. Google.
  21. OscaR Team (2012): OscaR: Scala in OR.
  22. Laurent Perron, Paul Shaw & Vincent Furnon (2004): Propagation Guided Large Neighborhood Search. In: Principles and Practice of Constraint Programming CP 2004, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp. 468–481, doi:10.1007/978-3-540-30201-8_35.
  23. David Pisinger & Stefan Røpke (2010): Large Neighborhood Search. In: Handbook of Metaheuristics. Springer, pp. 399–420, doi:10.1016/j.ejor.2004.08.015.
  24. Charles Prud'homme, Jean-Guillaume Fages & Xavier Lorca (2017): Choco Documentation. TASC - LS2N CNRS UMR 6241, COSLING S.A.S.. Available at http://www.choco-solver.org.
  25. Andrea Rendl, Tias Guns, Peter J. Stuckey & Guido Tack (2015): MiniSearch: A Solver-Independent Meta-Search Language for MiniZinc. In: Gilles Pesant: Principles and Practice of Constraint Programming 9255. Springer International Publishing, Cham, pp. 376–392, doi:10.1007/978-3-319-23219-5_27.
  26. Andrea Schaerf, Maurizio Lenzerini & Marco Cadoli (1999): LOCAL++: a C++ framework for local search algorithms. In: Proceedings Technology of Object-Oriented Languages and Systems. TOOLS 29 (Cat. No.PR00275), pp. 152–161, doi:10.1109/TOOLS.1999.779008.
  27. Bart Selman, Henry A. Kautz & Bram Cohen (1994): Noise Strategies for Improving Local Search. In: Barbara Hayes-Roth & Richard E. Korf: Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31 - August 4, 1994, Volume 1.. AAAI Press / The MIT Press, pp. 337–343. Available at http://www.aaai.org/Library/AAAI/1994/aaai94-051.php.
  28. B. Shahriari, K. Swersky, Z. Wang, R. P. Adams & N. de Freitas (2016): Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104(1), pp. 148–175, doi:10.1109/JPROC.2015.2494218.
  29. Mateusz \'Slażyński, Salvador Abreu & Grzegorz J. Nalepa (2019): Generating Local Search Neighborhood with Synthesized Logic Programs. In: ICLP 2019, Electronic Proceedings in Theoretical Computer Science, Las Cruces, New Mexico, USA. Forthcoming.
  30. Mateusz \'Slażyński, Salvador Abreu & Grzegorz J. Nalepa (2019): Towards a Formal Specification of Local Search Neighborhoods from a Constraint Satisfaction Problem Structure. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '19. ACM, New York, NY, USA, pp. 137–138, doi:10.1145/3319619.3321968.
  31. Peter J. Stuckey, Thibaut Feydy, Andreas Schutt, Guido Tack & Julien Fischer (2014): The MiniZinc Challenge 20082013. AI Magazine 35(2), pp. 55–60, doi:10.1609/aimag.v35i2.2539.
  32. Charlotte Truchet & Philippe Codognet (2004): Musical constraint satisfaction problems solved with adaptive search. Soft Comput. 8(9), pp. 633–640, doi:10.1007/s00500-004-0389-0.
  33. Pascal Van Hentenryck (1999): The OPL Optimization Programming Language. MIT Press, Cambridge, MA, USA.

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