@Inbook{Lourenço2003, author="Louren{\c{c}}o, Helena R. and Martin, Olivier C. and St{\"u}tzle, Thomas", editor="Glover, Fred and Kochenberger, Gary A.", title="Iterated Local Search", bookTitle="Handbook of Metaheuristics", year="2003", publisher="Springer US", address="Boston, MA", pages="320--353", isbn="978-0-306-48056-0", doi="10.1007/0-306-48056-5_11", url="https://doi.org/10.1007/0-306-48056-5_11" } @article{DBLP:journals/tec/ZhangL07, author = {Qingfu Zhang and Hui Li}, title = {{MOEA/D:} {A} Multiobjective Evolutionary Algorithm Based on Decomposition}, journal = {{IEEE} Transactions on Evolutionary Computation}, volume = {11}, number = {6}, pages = {712--731}, year = {2007}, url = {https://doi.org/10.1109/TEVC.2007.892759}, doi = {10.1109/TEVC.2007.892759}, timestamp = {Tue, 12 May 2020 16:51:09 +0200}, biburl = {https://dblp.org/rec/journals/tec/ZhangL07.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{DBLP:journals/cor/AlvesA07, author = {Maria Jo{\~{a}}o Alves and Marla Almeida}, title = {{MOTGA:} {A} multiobjective {T}chebycheff based genetic algorithm for the multidimensional knapsack problem}, journal = {Computers & Operations Research}, volume = {34}, number = {11}, pages = {3458--3470}, year = {2007}, url = {https://doi.org/10.1016/j.cor.2006.02.008}, doi = {10.1016/j.cor.2006.02.008}, timestamp = {Tue, 18 Feb 2020 13:56:37 +0100}, biburl = {https://dblp.org/rec/journals/cor/AlvesA07.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{DBLP:journals/tec/LiFKZ14, author = {Ke Li and \'{A}lvaro Fialho and Sam Kwong and Qingfu Zhang}, title = {Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition}, journal = {{IEEE} Transactions on Evolutionary Computation}, volume = {18}, number = {1}, pages = {114--130}, year = {2014}, url = {https://doi.org/10.1109/TEVC.2013.2239648}, doi = {10.1109/TEVC.2013.2239648}, timestamp = {Tue, 12 May 2020 16:50:56 +0200}, biburl = {https://dblp.org/rec/journals/tec/LiFKZ14.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{kim2005adaptive, title={Adaptive weighted-sum method for bi-objective optimization: Pareto front generation}, author={Kim, Il Yong and De Weck, Oliver L}, journal={Structural and multidisciplinary optimization}, volume={29}, number={2}, pages={149--158}, year={2005}, publisher={Springer}, url = {https://doi.org/10.1007/s00158-004-0465-1}, doi = {10.1007/s00158-004-0465-1} } @inproceedings{DBLP:conf/icmla/ChenJ07, author = {Xue{-}wen Chen and Jong Cheol Jeong}, editor = {M. Arif Wani and Mehmed M. Kantardzic and Tao Li and Ying Liu and Lukasz A. Kurgan and Jieping Ye and Mitsunori Ogihara and Seref Sagiroglu and Xue{-}wen Chen and Leif E. Peterson and Khalid Hafeez}, title = {Enhanced recursive feature elimination}, booktitle = {The Sixth International Conference on Machine Learning and Applications, {ICMLA} 2007, Cincinnati, Ohio, USA, 13-15 December 2007}, pages = {429--435}, publisher = {{IEEE} Computer Society}, year = {2007}, url = {https://doi.org/10.1109/ICMLA.2007.35}, doi = {10.1109/ICMLA.2007.35}, timestamp = {Wed, 16 Oct 2019 14:14:53 +0200}, biburl = {https://dblp.org/rec/conf/icmla/ChenJ07.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{DBLP:journals/remotesensing/PullanagariKY18, author = {Rajasheker R. Pullanagari and Gabor Kereszturi and Ian Yule}, title = {Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression}, journal = {Remote Sensing}, volume = {10}, number = {7}, pages = {1117}, year = {2018}, url = {https://doi.org/10.3390/rs10071117}, doi = {10.3390/rs10071117}, timestamp = {Mon, 15 Jun 2020 16:51:53 +0200}, biburl = {https://dblp.org/rec/journals/remotesensing/PullanagariKY18.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @misc{ceres-solver, author = "Sameer Agarwal and Keir Mierle and Others", title = "Ceres Solver", version = "2.0.0", year = "2020", howpublished = "\url{http://ceres-solver.org}", } @book{hart2017pyomo, title={Pyomo--optimization modeling in python}, author={Hart, William E. and Laird, Carl D. and Watson, Jean-Paul and Woodruff, David L. and Hackebeil, Gabriel A. and Nicholson, Bethany L. and Siirola, John D.}, edition={Second Edition}, volume={67}, year={2017}, publisher={Springer Science \& Business Media} } @article{pyopt-paper, author = {Ruben E. Perez and Peter W. Jansen and Joaquim R. R. A. Martins}, title = {py{O}pt: A {P}ython-Based Object-Oriented Framework for Nonlinear Constrained Optimization}, journal = {Structures and Multidisciplinary Optimization}, year = {2012}, volume = {45}, number = {1}, pages = {101--118}, doi = {10.1007/s00158-011-0666-3} } @InProceedings{10.1007/978-3-319-42432-3_37, author="Maher, Stephen and Miltenberger, Matthias and Pedroso, Jo{\~a}o Pedro and Rehfeldt, Daniel and Schwarz, Robert and Serrano, Felipe", editor="Greuel, Gert-Martin and Koch, Thorsten and Paule, Peter and Sommese, Andrew", title="PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite", booktitle="Mathematical Software -- ICMS 2016", year="2016", publisher="Springer International Publishing", address="Cham", pages="301--307", abstract="SCIP is a solver for a wide variety of mathematical optimization problems. It is written in C and extendable due to its plug-in based design. However, dealing with all C specifics when extending SCIP can be detrimental to development and testing of new ideas. This paper attempts to provide a remedy by introducing PySCIPOpt, a Python interface to SCIP that enables users to write new SCIP code entirely in Python. We demonstrate how to intuitively model mixed-integer linear and quadratic optimization problems and moreover provide examples on how new Python plug-ins can be added to SCIP.", isbn="978-3-319-42432-3", doi="10.1007/978-3-319-42432-3_37" } @misc{simanneal-solver, author = "Matthew Perry", title = "simanneal", year= "2019", version= "0.5.0", publisher = {GitHub}, journal = {GitHub repository}, howpublished = "\url{https://github.com/perrygeo/simanneal}", } @misc{solid-solver, author = "Devin Soni", title = "Solid", version = "0.11", year = "2017", publisher = {GitHub}, journal = {GitHub repository}, howpublished = "\url{https://github.com/100/Solid}", } @inproceedings{10.1145/3321707.3321800, author = {Lepr\^{e}tre, Florian and Verel, S\'{e}bastien and Fonlupt, Cyril and Marion, Virginie}, title = {Walsh Functions as Surrogate Model for Pseudo-Boolean Optimization Problems}, year = {2019}, isbn = {9781450361118}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3321707.3321800}, doi = {10.1145/3321707.3321800}, abstract = {Surrogate-modeling is about formulating quick-to-evaluate mathematical models, to approximate black-box and time-consuming computations or simulation tasks. Although such models are well-established to solve continuous optimization problems, very few investigations regard the optimization of combinatorial structures. These structures deal for instance with binary variables, allowing each compound in the representation of a solution to be activated or not. Still, this field of research is experiencing a sudden renewed interest, bringing to the community fresh algorithmic ideas for growing these particular surrogate models. This article proposes the first surrogate-assisted optimization algorithm (WSaO) based on the mathematical foundations of discrete Walsh functions, combined with the powerful grey-box optimization techniques in order to solve pseudo-boolean optimization problems. We conduct our experiments on a benchmark of combinatorial structures and demonstrate the accuracy, and the optimization efficiency of the proposed model. We finally highlight how Walsh surrogates may outperform the state-of-the-art surrogate models for pseudo-boolean functions.}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, pages = {303–311}, numpages = {9}, keywords = {combinatorial optimization, surrogate model/fitness approximation, local search, empirical study}, location = {Prague, Czech Republic}, series = {GECCO '19} }