paper.bib 9.0 KB

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  1. @Inbook{Lourenço2003,
  2. author="Louren{\c{c}}o, Helena R.
  3. and Martin, Olivier C.
  4. and St{\"u}tzle, Thomas",
  5. editor="Glover, Fred
  6. and Kochenberger, Gary A.",
  7. title="Iterated Local Search",
  8. bookTitle="Handbook of Metaheuristics",
  9. year="2003",
  10. publisher="Springer US",
  11. address="Boston, MA",
  12. pages="320--353",
  13. isbn="978-0-306-48056-0",
  14. doi="10.1007/0-306-48056-5_11",
  15. url="https://doi.org/10.1007/0-306-48056-5_11"
  16. }
  17. @article{DBLP:journals/tec/ZhangL07,
  18. author = {Qingfu Zhang and
  19. Hui Li},
  20. title = {{MOEA/D:} {A} Multiobjective Evolutionary Algorithm Based on Decomposition},
  21. journal = {{IEEE} Transactions on Evolutionary Computation},
  22. volume = {11},
  23. number = {6},
  24. pages = {712--731},
  25. year = {2007},
  26. url = {https://doi.org/10.1109/TEVC.2007.892759},
  27. doi = {10.1109/TEVC.2007.892759},
  28. timestamp = {Tue, 12 May 2020 16:51:09 +0200},
  29. biburl = {https://dblp.org/rec/journals/tec/ZhangL07.bib},
  30. bibsource = {dblp computer science bibliography, https://dblp.org}
  31. }
  32. @article{DBLP:journals/cor/AlvesA07,
  33. author = {Maria Jo{\~{a}}o Alves and
  34. Marla Almeida},
  35. title = {{MOTGA:} {A} multiobjective {T}chebycheff based genetic algorithm for
  36. the multidimensional knapsack problem},
  37. journal = {Computers & Operations Research},
  38. volume = {34},
  39. number = {11},
  40. pages = {3458--3470},
  41. year = {2007},
  42. url = {https://doi.org/10.1016/j.cor.2006.02.008},
  43. doi = {10.1016/j.cor.2006.02.008},
  44. timestamp = {Tue, 18 Feb 2020 13:56:37 +0100},
  45. biburl = {https://dblp.org/rec/journals/cor/AlvesA07.bib},
  46. bibsource = {dblp computer science bibliography, https://dblp.org}
  47. }
  48. @article{DBLP:journals/tec/LiFKZ14,
  49. author = {Ke Li and
  50. \'{A}lvaro Fialho and
  51. Sam Kwong and
  52. Qingfu Zhang},
  53. title = {Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary
  54. Algorithm Based on Decomposition},
  55. journal = {{IEEE} Transactions on Evolutionary Computation},
  56. volume = {18},
  57. number = {1},
  58. pages = {114--130},
  59. year = {2014},
  60. url = {https://doi.org/10.1109/TEVC.2013.2239648},
  61. doi = {10.1109/TEVC.2013.2239648},
  62. timestamp = {Tue, 12 May 2020 16:50:56 +0200},
  63. biburl = {https://dblp.org/rec/journals/tec/LiFKZ14.bib},
  64. bibsource = {dblp computer science bibliography, https://dblp.org}
  65. }
  66. @article{kim2005adaptive,
  67. title={Adaptive weighted-sum method for bi-objective optimization: Pareto front generation},
  68. author={Kim, Il Yong and De Weck, Oliver L},
  69. journal={Structural and multidisciplinary optimization},
  70. volume={29},
  71. number={2},
  72. pages={149--158},
  73. year={2005},
  74. publisher={Springer},
  75. url = {https://doi.org/10.1007/s00158-004-0465-1},
  76. doi = {10.1007/s00158-004-0465-1}
  77. }
  78. @inproceedings{DBLP:conf/icmla/ChenJ07,
  79. author = {Xue{-}wen Chen and
  80. Jong Cheol Jeong},
  81. editor = {M. Arif Wani and
  82. Mehmed M. Kantardzic and
  83. Tao Li and
  84. Ying Liu and
  85. Lukasz A. Kurgan and
  86. Jieping Ye and
  87. Mitsunori Ogihara and
  88. Seref Sagiroglu and
  89. Xue{-}wen Chen and
  90. Leif E. Peterson and
  91. Khalid Hafeez},
  92. title = {Enhanced recursive feature elimination},
  93. booktitle = {The Sixth International Conference on Machine Learning and Applications,
  94. {ICMLA} 2007, Cincinnati, Ohio, USA, 13-15 December 2007},
  95. pages = {429--435},
  96. publisher = {{IEEE} Computer Society},
  97. year = {2007},
  98. url = {https://doi.org/10.1109/ICMLA.2007.35},
  99. doi = {10.1109/ICMLA.2007.35},
  100. timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
  101. biburl = {https://dblp.org/rec/conf/icmla/ChenJ07.bib},
  102. bibsource = {dblp computer science bibliography, https://dblp.org}
  103. }
  104. @article{DBLP:journals/remotesensing/PullanagariKY18,
  105. author = {Rajasheker R. Pullanagari and
  106. Gabor Kereszturi and
  107. Ian Yule},
  108. title = {Integrating Airborne Hyperspectral, Topographic, and Soil Data for
  109. Estimating Pasture Quality Using Recursive Feature Elimination with
  110. Random Forest Regression},
  111. journal = {Remote Sensing},
  112. volume = {10},
  113. number = {7},
  114. pages = {1117},
  115. year = {2018},
  116. url = {https://doi.org/10.3390/rs10071117},
  117. doi = {10.3390/rs10071117},
  118. timestamp = {Mon, 15 Jun 2020 16:51:53 +0200},
  119. biburl = {https://dblp.org/rec/journals/remotesensing/PullanagariKY18.bib},
  120. bibsource = {dblp computer science bibliography, https://dblp.org}
  121. }
  122. @misc{ceres-solver,
  123. author = "Sameer Agarwal and Keir Mierle and Others",
  124. title = "Ceres Solver",
  125. version = "2.0.0",
  126. year = "2020",
  127. howpublished = "\url{http://ceres-solver.org}",
  128. }
  129. @book{hart2017pyomo,
  130. title={Pyomo--optimization modeling in python},
  131. 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.},
  132. edition={Second Edition},
  133. volume={67},
  134. year={2017},
  135. publisher={Springer Science \& Business Media}
  136. }
  137. @article{pyopt-paper,
  138. author = {Ruben E. Perez and Peter W. Jansen and Joaquim R. R. A. Martins},
  139. title = {py{O}pt: A {P}ython-Based Object-Oriented Framework for Nonlinear Constrained Optimization},
  140. journal = {Structures and Multidisciplinary Optimization},
  141. year = {2012},
  142. volume = {45},
  143. number = {1},
  144. pages = {101--118},
  145. doi = {10.1007/s00158-011-0666-3}
  146. }
  147. @InProceedings{10.1007/978-3-319-42432-3_37,
  148. author="Maher, Stephen
  149. and Miltenberger, Matthias
  150. and Pedroso, Jo{\~a}o Pedro
  151. and Rehfeldt, Daniel
  152. and Schwarz, Robert
  153. and Serrano, Felipe",
  154. editor="Greuel, Gert-Martin
  155. and Koch, Thorsten
  156. and Paule, Peter
  157. and Sommese, Andrew",
  158. title="PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite",
  159. booktitle="Mathematical Software -- ICMS 2016",
  160. year="2016",
  161. publisher="Springer International Publishing",
  162. address="Cham",
  163. pages="301--307",
  164. 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.",
  165. isbn="978-3-319-42432-3",
  166. doi="10.1007/978-3-319-42432-3_37"
  167. }
  168. @misc{simanneal-solver,
  169. author = "Matthew Perry",
  170. title = "simanneal",
  171. year= "2019",
  172. version= "0.5.0",
  173. publisher = {GitHub},
  174. journal = {GitHub repository},
  175. howpublished = "\url{https://github.com/perrygeo/simanneal}",
  176. }
  177. @misc{solid-solver,
  178. author = "Devin Soni",
  179. title = "Solid",
  180. version = "0.11",
  181. year = "2017",
  182. publisher = {GitHub},
  183. journal = {GitHub repository},
  184. howpublished = "\url{https://github.com/100/Solid}",
  185. }
  186. @inproceedings{10.1145/3321707.3321800,
  187. author = {Lepr\^{e}tre, Florian and Verel, S\'{e}bastien and Fonlupt, Cyril and Marion, Virginie},
  188. title = {Walsh Functions as Surrogate Model for Pseudo-Boolean Optimization Problems},
  189. year = {2019},
  190. isbn = {9781450361118},
  191. publisher = {Association for Computing Machinery},
  192. address = {New York, NY, USA},
  193. url = {https://doi.org/10.1145/3321707.3321800},
  194. doi = {10.1145/3321707.3321800},
  195. 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.},
  196. booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
  197. pages = {303–311},
  198. numpages = {9},
  199. keywords = {combinatorial optimization, surrogate model/fitness approximation, local search, empirical study},
  200. location = {Prague, Czech Republic},
  201. series = {GECCO '19}
  202. }