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.
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.
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.
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.
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.
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.
Pascal Van Hentenryck & Laurent Michel (2005):
Constraint-based local search.
MIT Press.
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.
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.
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.
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.
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.
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.
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.
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.
Nikolaj van Omme, Laurent Perron & Vincent Furnon (2014):
or-tools user's manual.
Technical Report.
Google.
OscaR Team (2012):
OscaR: Scala in OR.
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.
David Pisinger & Stefan Røpke (2010):
Large Neighborhood Search.
In: Handbook of Metaheuristics.
Springer,
pp. 399–420,
doi:10.1016/j.ejor.2004.08.015.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Pascal Van Hentenryck (1999):
The OPL Optimization Programming Language.
MIT Press,
Cambridge, MA, USA.