Thesis-OptimizationModules/paper.bib

@incollection{DBLP:books/sp/03/LourencoMS03,
  author    = {Helena R. Louren{\c{c}}o and
               Olivier C. Martin and
               Thomas St{\"{u}}tzle},
  editor    = {Fred W. Glover and
               Gary A. Kochenberger},
  title     = {Iterated Local Search},
  booktitle = {Handbook of Metaheuristics},
  series    = {International Series in Operations Research {\&} Management Science},
  volume    = {57},
  pages     = {320--353},
  publisher = {Kluwer / Springer},
  year      = {2003},
  url       = {https://doi.org/10.1007/0-306-48056-5\_11},
  doi       = {10.1007/0-306-48056-5\_11},
  timestamp = {Mon, 15 Jun 2020 16:48:23 +0200},
  biburl    = {https://dblp.org/rec/books/sp/03/LourencoMS03.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tec/ZhangL07,
  author    = {Qingfu Zhang and
               Hui Li},
  title     = {{MOEA/D:} {A} Multiobjective Evolutionary Algorithm Based on Decomposition},
  journal   = {{IEEE} Trans. Evol. Comput.},
  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 Tchebycheff based genetic algorithm for
               the multidimensional knapsack problem},
  journal   = {Comput. Oper. Res.},
  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} Trans. Evol. Comput.},
  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. Sens.},
  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},
  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}
}

@incollection{MaherMiltenbergerPedrosoRehfeldtSchwarzSerrano2016,
  author = {Stephen Maher and Matthias Miltenberger and Jo{\~{a}}o Pedro Pedroso and Daniel Rehfeldt and Robert Schwarz and Felipe Serrano},
  title = {{PySCIPOpt}: Mathematical Programming in Python with the {SCIP} Optimization Suite},
  booktitle = {Mathematical Software {\textendash} {ICMS} 2016},
  publisher = {Springer International Publishing},
  pages = {301--307},
  year = {2016},
  doi = {10.1007/978-3-319-42432-3_37},
}

@misc{simanneal-solver,
  author = "Matthew Perry",
  title = "simanneal",
  year= "2019",
  version= "0.5.0",
  howpublished = "\url{https://github.com/perrygeo/simanneal}",
}

@misc{solid-solver,
  author = "Devin Soni",
  title = "Solid",
  version = "0.11",
  year = "2017",
  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}
}