bibliography.bib

@article{abs181207958,
  author        = {Pei Cao, Jiong Tang},
  title         = {A Reinforcement Learning Hyper-Heuristic in Multi-Objective Single
			Point Search with Application to Structural Fault Identification},
  journal       = {CoRR},
  volume        = {abs/1812.07958},
  year          = {2018},
  url           = {http://arxiv.org/abs/1812.07958},
  archiveprefix = {arXiv},
  eprint        = {1812.07958},
  timestamp     = {Wed, 02 Jan 2019 14:40:18 +0100},
  biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1812-07958},
  bibsource     = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Audet2018PerformanceII,
  title  = {Performance indicators in multiobjective optimization},
  author = {Charles Audet and S{\'e}bastien Le Digabel and Dominique Cartier and Jean Bigeon and Ludovic Salomon},
  year   = {2018}
}

@inproceedings{autosklearn:feurer2015efficient,
  title     = {Efficient and robust automated machine learning},
  author    = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
  booktitle = {Advances in neural information processing systems},
  pages     = {2962--2970},
  year      = {2015}
}

@misc{benitezhidalgo2019jmetalpy,
  title         = {jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics},
  author        = {Antonio Benitez-Hidalgo and Antonio J. Nebro and Jose Garcia-Nieto and Izaskun Oregi and Javier Del Ser},
  year          = {2019},
  eprint        = {1903.02915},
  archiveprefix = {arXiv},
  primaryclass  = {cs.NE}
}

@misc{bodin2019modulating,
  title         = {Modulating Surrogates for Bayesian Optimization},
  author        = {Erik Bodin and Markus Kaiser and Ieva Kazlauskaite and Zhenwen Dai and Neill D. F. Campbell and Carl Henrik Ek},
  year          = {2019},
  eprint        = {1906.11152},
  archiveprefix = {arXiv},
  primaryclass  = {stat.ML}
}

@misc{buitinck2013api,
  title         = {API design for machine learning software: experiences from the scikit-learn project},
  author        = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and Fabian Pedregosa and Andreas Mueller and Olivier Grisel and Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort and Jaques Grobler and Robert Layton and Jake Vanderplas and Arnaud Joly and Brian Holt and Gaël Varoquaux},
  year          = {2013},
  eprint        = {1309.0238},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG}
}

@article{ChafekarSRX05,
  author    = {Deepti Chafekar and
			Liang Shi and
			Khaled Rasheed and
			Jiang Xuan},
  title     = {Multiobjective {GA} optimization using reduced models},
  journal   = {{IEEE} Trans. Systems, Man, and Cybernetics, Part {C}},
  volume    = {35},
  number    = {2},
  pages     = {261--265},
  year      = {2005},
  url       = {https://doi.org/10.1109/TSMCC.2004.841905},
  doi       = {10.1109/TSMCC.2004.841905},
  timestamp = {Wed, 17 May 2017 10:55:49 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/tsmc/ChafekarSRX05},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{CustodioMVV11,
  author    = {A. L. Cust{\'{o}}dio and
	J. F. Aguilar Madeira and
	A. Ismael F. Vaz and
	Lu{\'{\i}}s Nunes Vicente},
  title     = {Direct Multisearch for Multiobjective Optimization},
  journal   = {{SIAM} Journal on Optimization},
  volume    = {21},
  number    = {3},
  pages     = {1109--1140},
  year      = {2011},
  url       = {https://doi.org/10.1137/10079731X},
  doi       = {10.1137/10079731X},
  timestamp = {Wed, 15 Jan 2020 18:09:46 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/siamjo/CustodioMVV11},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@incollection{DebN07,
  author    = {Kalyanmoy Deb and
			Pawan K. S. Nain},
  title     = {An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using
			Artificial Neural Networks},
  booktitle = {Evolutionary Computation in Dynamic and Uncertain Environments},
  pages     = {297--322},
  year      = {2007},
  url       = {https://doi.org/10.1007/978-3-540-49774-5\_13},
  doi       = {10.1007/978-3-540-49774-5\_13},
  timestamp = {Tue, 16 May 2017 14:24:31 +0200},
  biburl    = {https://dblp.org/rec/bib/series/sci/DebN07},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@incollection{DebTLZ05,
  author    = {Kalyanmoy Deb and
			Lothar Thiele and
			Marco Laumanns and
			Eckart Zitzler},
  title     = {Scalable Test Problems for Evolutionary Multiobjective Optimization},
  booktitle = {Evolutionary Multiobjective Optimization},
  pages     = {105--145},
  year      = {2005},
  url       = {https://doi.org/10.1007/1-84628-137-7\_6},
  doi       = {10.1007/1-84628-137-7\_6},
  timestamp = {Thu, 25 Jul 2019 18:49:32 +0200},
  biburl    = {https://dblp.org/rec/bib/books/sp/05/DebTLZ05},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{EmmerichD18,
  author    = {Michael T. M. Emmerich and
               Andr{\'{e}} H. Deutz},
  title     = {A tutorial on multiobjective optimization: fundamentals and evolutionary
               methods},
  journal   = {Natural Computing},
  volume    = {17},
  number    = {3},
  pages     = {585--609},
  year      = {2018},
  url       = {https://doi.org/10.1007/s11047-018-9685-y},
  doi       = {10.1007/s11047-018-9685-y},
  timestamp = {Wed, 03 Oct 2018 12:54:55 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/nc/EmmerichD18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{EmmerichGN06,
  author    = {Michael T. M. Emmerich and
			Kyriakos C. Giannakoglou and
			Boris Naujoks},
  title     = {Single- and multiobjective evolutionary optimization assisted by Gaussian
			random field metamodels},
  journal   = {{IEEE} Trans. Evolutionary Computation},
  volume    = {10},
  number    = {4},
  pages     = {421--439},
  year      = {2006},
  url       = {https://doi.org/10.1109/TEVC.2005.859463},
  doi       = {10.1109/TEVC.2005.859463},
  timestamp = {Wed, 17 May 2017 14:25:39 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/tec/EmmerichGN06},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{EngSurMod,
  added-at  = {2011-03-15T00:00:00.000+0100},
  author    = {Forrester, Alexander I. J. and Sobester, Andras and Keane, Andy J.},
  biburl    = {https://www.bibsonomy.org/bibtex/2e50e273c544b70923fd89bf1798f28cf/dblp},
  ee        = {http://onlinelibrary.wiley.com/book/10.1002/9780470770801},
  interhash = {19127ebd3e6c7ddd1ea4b8614978ab3d},
  intrahash = {e50e273c544b70923fd89bf1798f28cf},
  isbn      = {978-0-470-06068-1},
  keywords  = {dblp},
  pages     = {I-XVIII, 1-210},
  publisher = {Wiley},
  timestamp = {2011-04-29T15:26:06.000+0200},
  title     = {Engineering Design via Surrogate Modelling - A Practical Guide.},
  year      = 2008
}


@software{francesco_biscani_2019,
  author    = {Francesco Biscani and
			Dario Izzo and
			Wenzel Jakob and
			GiacomoAcciarini and
			Marcus Märtens and
			Micky C and
			Alessio Mereta and
			Cord Kaldemeyer and
			Sergey Lyskov and
			Giacomo Acciarini and
			Sylvain Corlay and
			acxz and
			Benjamin Pritchard and
			Kishan Manani and
			Johan Mabille and
			Axel Huebl and
			Manuel López-Ibáñez and
			jakirkham and
			Jeongseok Lee and
			hulucc and
			polygon and
			Luka Čehovin Zajc and
			Jonas Adler and
			John Travers and
			Jakob Jordan and
			Ivan Smirnov and
			Huu Nguyen and
			Felipe Lema and
			Erik O'Leary and
			Andrea Mambrini},
  title     = {esa/pagmo2: pagmo 2.12.0},
  month     = dec,
  year      = 2019,
  publisher = {Zenodo},
  version   = {v2.12.0},
  doi       = {10.5281/zenodo.3582877},
  url       = {https://doi.org/10.5281/zenodo.3582877}
}

@article{art-scikit-learn,
  title={{Scikit-learn: Machine Learning in Python }},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
      and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
      and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
      Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}

@article{Grierson1993,
  author   = {Grierson, D. E.
	and Pak, W. H.},
  title    = {Optimal sizing, geometrical and topological design using a genetic algorithm},
  journal  = {Structural optimization},
  year     = {1993},
  month    = {Sep},
  day      = {01},
  volume   = {6},
  number   = {3},
  pages    = {151--159},
  abstract = {A genetic algorithm is applied for the optimal design of skeletal building structures accounting for discrete sizing, geometrical and topological variables. An approximate designfitness evaluation technique is investigated with the aim to improve the numerical efficiency of the genetic search. Two design examples are presented to illustrate the principles involved.},
  issn     = {1615-1488},
  doi      = {10.1007/BF01743506},
  url      = {https://doi.org/10.1007/BF01743506}
}

@article{Jin05,
  author    = {Yaochu Jin},
  title     = {A comprehensive survey of fitness approximation in evolutionary computation},
  journal   = {Soft Comput.},
  volume    = {9},
  number    = {1},
  pages     = {3--12},
  year      = {2005},
  url       = {https://doi.org/10.1007/s00500-003-0328-5},
  doi       = {10.1007/s00500-003-0328-5},
  timestamp = {Sat, 20 May 2017 00:25:41 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/soco/Jin05},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{kouwe2018benchmarking,
  title         = {Benchmarking Crimes: An Emerging Threat in Systems Security},
  author        = {Erik van der Kouwe and Dennis Andriesse and Herbert Bos and Cristiano Giuffrida and Gernot Heiser},
  year          = {2018},
  eprint        = {1801.02381},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CR}
}


@article{KrallMD15,
  author    = {Joseph Krall and
			Tim Menzies and
			Misty Davies},
  title     = {{GALE:} Geometric Active Learning for Search-Based Software Engineering},
  journal   = {{IEEE} Trans. Software Eng.},
  volume    = {41},
  number    = {10},
  pages     = {1001--1018},
  year      = {2015},
  url       = {https://doi.org/10.1109/TSE.2015.2432024},
  doi       = {10.1109/TSE.2015.2432024},
  timestamp = {Wed, 14 Nov 2018 10:49:20 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/tse/KrallMD15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Li2009,
  author   = {Li, G.
	and Li, M.
	and Azarm, S.
	and Al Hashimi, S.
	and Al Ameri, T.
	and Al Qasas, N.},
  title    = {Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling},
  journal  = {Structural and Multidisciplinary Optimization},
  year     = {2009},
  month    = {Feb},
  day      = {01},
  volume   = {37},
  number   = {5},
  pages    = {447--461},
  abstract = {Applications of multi-objective genetic algorithms (MOGAs) in engineering optimization problems often require numerous function calls. One way to reduce the number of function calls is to use an approximation in lieu of function calls. An approximation involves two steps: design of experiments (DOE) and metamodeling. This paper presents a new approach where both DOE and metamodeling are integrated with a MOGA. In particular, the DOE method reduces the number of generations in a MOGA, while the metamodeling reduces the number of function calls in each generation. In the present approach, the DOE locates a subset of design points that is estimated to better sample the design space, while the metamodeling assists in estimating the fitness of design points. Several numerical and engineering examples are used to demonstrate the applicability of this new approach. The results from these examples show that the proposed improved approach requires significantly fewer function calls and obtains similar solutions compared to a conventional MOGA and a recently developed metamodeling-assisted MOGA.},
  issn     = {1615-1488},
  doi      = {10.1007/s00158-008-0251-6},
  url      = {https://doi.org/10.1007/s00158-008-0251-6}
}

@article{MlakarPTF15,
  author    = {Miha Mlakar and
			Dejan Petelin and
			Tea Tusar and
			Bogdan Filipic},
  title     = {{GP-DEMO:} Differential Evolution for Multiobjective Optimization
			based on Gaussian Process models},
  journal   = {European Journal of Operational Research},
  volume    = {243},
  number    = {2},
  pages     = {347--361},
  year      = {2015},
  url       = {https://doi.org/10.1016/j.ejor.2014.04.011},
  doi       = {10.1016/j.ejor.2014.04.011},
  timestamp = {Sat, 27 May 2017 14:25:14 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/eor/MlakarPTF15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Nakayama05,
  author    = {Hirotaka Nakayama},
  title     = {Multi-objective Optimization and its Engineering Applications},
  booktitle = {Practical Approaches to Multi-Objective Optimization, 7.-12. November 2004},
  year      = {2005},
  url       = {http://drops.dagstuhl.de/opus/volltexte/2005/234},
  timestamp = {Thu, 23 Aug 2018 15:56:31 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/dagstuhl/Nakayama05},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{odugod2013,
  author  = {Odu, Godwin},
  year    = {2013},
  month   = {10},
  pages   = {01-14},
  title   = {Review of Multi-criteria Optimization Methods – Theory and Applications},
  volume  = {3},
  journal = {IOSR Journal of Engineering},
  doi     = {10.9790/3021-031020114}
}

@inproceedings{OlsonGECCO2016,
  author    = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan J. and Moore, Jason H.},
  title     = {Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016},
  series    = {GECCO '16},
  year      = {2016},
  isbn      = {978-1-4503-4206-3},
  location  = {Denver, Colorado, USA},
  pages     = {485--492},
  numpages  = {8},
  url       = {http://doi.acm.org/10.1145/2908812.2908918},
  doi       = {10.1145/2908812.2908918},
  acmid     = {2908918},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@inproceedings{PilatN12,
  author    = {Martin Pil{\'{a}}t and
				Roman Neruda},
  title     = {An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization},
  booktitle = {Proceedings of the {IEEE} Congress on Evolutionary Computation, {CEC}
				2012, Brisbane, Australia, June 10-15, 2012},
  pages     = {1--7},
  year      = {2012},
  url       = {https://doi.org/10.1109/CEC.2012.6256450},
  doi       = {10.1109/CEC.2012.6256450},
  timestamp = {Wed, 16 Oct 2019 14:14:52 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/cec/PilatN12},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{TianPlatEMO,
  title   = {{PlatEMO}: A {MATLAB} platform for evolutionary multi-objective optimization},
  author  = {Tian, Ye and Cheng, Ran and Zhang, Xingyi and Jin, Yaochu},
  journal = {IEEE Computational Intelligence Magazine},
  volume  = {12},
  number  = {4},
  pages   = {73--87},
  year    = {2017}
}

@inbook{Rasmussen2004,
  author    = {Rasmussen, Carl Edward},
  editor    = {Bousquet, Olivier
	and von Luxburg, Ulrike
	and R{\"a}tsch, Gunnar},
  title     = {Gaussian Processes in Machine Learning},
  booktitle = {Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, T{\"u}bingen, Germany, August 4 - 16, 2003, Revised Lectures},
  year      = {2004},
  publisher = {Springer Berlin Heidelberg},
  address   = {Berlin, Heidelberg},
  pages     = {63--71},
  abstract  = {We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.},
  isbn      = {978-3-540-28650-9},
  doi       = {10.1007/978-3-540-28650-9_4},
  url       = {https://doi.org/10.1007/978-3-540-28650-9_4}
}

@article{RasmussenN10,
  author    = {Carl Edward Rasmussen and
               Hannes Nickisch},
  title     = {Gaussian Processes for Machine Learning {(GPML)} Toolbox},
  journal   = {J. Mach. Learn. Res.},
  volume    = {11},
  pages     = {3011--3015},
  year      = {2010},
  url       = {http://portal.acm.org/citation.cfm?id=1953029},
  timestamp = {Wed, 10 Jul 2019 15:27:55 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/jmlr/RasmussenN10},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{RasmussenW06,
  author    = {Carl Edward Rasmussen and
			Christopher K. I. Williams},
  title     = {Gaussian processes for machine learning},
  series    = {Adaptive computation and machine learning},
  publisher = {{MIT} Press},
  year      = {2006},
  url       = {http://www.worldcat.org/oclc/61285753},
  isbn      = {026218253X},
  timestamp = {Wed, 26 Apr 2017 17:48:08 +0200},
  biburl    = {https://dblp.org/rec/bib/books/lib/RasmussenW06},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{ResponseSurface,
  author    = {Myers, Raymond H. and Montgomery, Douglas C.},
  title     = {Response Surface Methodology: Process and Product in Optimization Using Designed Experiments},
  year      = {1995},
  isbn      = {0471581003},
  publisher = {John Wiley and Sons, Inc.},
  address   = {USA},
  edition   = {1st}
}

@inproceedings{Schott1995FaultTD,
  title  = {Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.},
  author = {Jason R. Schott},
  year   = {1995}
}

@article{TaboadaBCW07,
  author    = {Heidi A. Taboada and
               Fatema Baheranwala and
               David W. Coit and
               Naruemon Wattanapongsakorn},
  title     = {Practical solutions for multi-objective optimization: An application
               to system reliability design problems},
  journal   = {Rel. Eng. and Sys. Safety},
  volume    = {92},
  number    = {3},
  pages     = {314--322},
  year      = {2007},
  url       = {https://doi.org/10.1016/j.ress.2006.04.014},
  doi       = {10.1016/j.ress.2006.04.014},
  timestamp = {Sat, 27 May 2017 14:25:20 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/ress/TaboadaBCW07},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{WFGref,
  author   = {S. {Huband} and P. {Hingston} and L. {Barone} and L. {While}},
  journal  = {IEEE Transactions on Evolutionary Computation},
  title    = {A review of multiobjective test problems and a scalable test problem toolkit},
  year     = {2006},
  volume   = {10},
  number   = {5},
  pages    = {477-506},
  keywords = {evolutionary computation;multiobjective test problems;scalable test problem toolkit;multiobjective evolutionary algorithm;test problem criteria;Evolutionary computation;Design optimization;Australia;Algorithm design and analysis;System testing;Turbines;Combustion;Product design;Petroleum;Pipelines;Evolutionary algorithms (EAs);multiobjective evolutionary algorithms;multiobjective optimization;multiobjective test problems},
  doi      = {10.1109/TEVC.2005.861417},
  issn     = {1941-0026},
  month    = {Oct}
}

@article{Woodard00,
  author    = {Roger Woodard},
  title     = {Interpolation of Spatial Data: Some Theory for Kriging},
  journal   = {Technometrics},
  volume    = {42},
  number    = {4},
  pages     = {436--437},
  year      = {2000},
  url       = {https://doi.org/10.1080/00401706.2000.10485731},
  doi       = {10.1080/00401706.2000.10485731},
  timestamp = {Sat, 27 May 2017 14:25:18 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/technometrics/Woodard00},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{ZhangL07,
  author    = {Qingfu Zhang and
			Hui Li},
  title     = {{MOEA/D:} {A} Multiobjective Evolutionary Algorithm Based on Decomposition},
  journal   = {{IEEE} Trans. 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 = {Sun, 23 Sep 2018 19:44:31 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/tec/ZhangL07},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{ZitzlerDT00,
  author    = {Eckart Zitzler and
			Kalyanmoy Deb and
			Lothar Thiele},
  title     = {Comparison of Multiobjective Evolutionary Algorithms: Empirical Results},
  journal   = {Evolutionary Computation},
  volume    = {8},
  number    = {2},
  pages     = {173--195},
  year      = {2000},
  url       = {https://doi.org/10.1162/106365600568202},
  doi       = {10.1162/106365600568202},
  timestamp = {Wed, 14 Nov 2018 10:13:00 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/ec/ZitzlerDT00},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{SoftSurvey,
  author = {Chugh, Tinkle and Sindhya, Karthik and Hakanen, Jussi and Miettinen, Kaisa},
  year = {2017},
  month = {12},
  pages = {},
  title = {A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms},
  journal = {Soft Computing},
  doi = {10.1007/s00500-017-2965-0}
}

@InProceedings{DistrSurr,
  author="Isaacs, Amitay
  and Ray, Tapabrata
  and Smith, Warren",
  editor="Randall, Marcus
  and Abbass, Hussein A.
  and Wiles, Janet",
  title="An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization",
  booktitle="Progress in Artificial Life",
  year="2007",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="257--268",
  abstract="In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for multiobjective optimization is presented. The algorithm performs actual analysis for the initial population and periodically every few generations. An external archive of the unique solutions evaluated using the actual analysis is maintained to train the surrogate models. The data points in the archive are split into multiple partitions using k-Means clustering. A Radial Basis Function (RBF) network surrogate model is built for each partition using a fraction of the points in that partition. The rest of the points in the partition are used as a validation data to decide the prediction accuracy of the surrogate model. Prediction of a new candidate solution is done by the surrogate model with the least prediction error in the neighborhood of that point. Five multiobjective test problems are presented in this study and a comparison with Nondominated Sorting Genetic Algorithm II (NSGA-II) is included to highlight the benefits offered by our approach. EASDS algorithm consistently reported better nondominated solutions for all the test cases for the same number of actual evaluations as compared to a single global surrogate model and NSGA-II.",
  isbn="978-3-540-76931-6"
}

@INPROCEEDINGS{HybridSurrRCG,
  author={A. {Rosales-Pérez} and C. A. C. {Coello} and J. A. {Gonzalez} and C. A. {Reyes-Garcia} and H. J. {Escalante}},
  booktitle={2013 IEEE Congress on Evolutionary Computation},
  title={A hybrid surrogate-based approach for evolutionary multi-objective optimization},
  year={2013},
  volume={},
  number={},
  pages={2548-2555},
  keywords={benchmark testing;evolutionary computation;Pareto optimisation;support vector machines;hybrid surrogate-based approach;evolutionary multiobjective optimization problems;evolutionary algorithms;fitness function evaluations;Pareto front approximation;evolutionary algorithm;surrogate models;support vector machines;SVM;model selection process;hyperparameter values;evolutionary process;surrogate fidelity;NSGA-II;Support vector machines;Sociology;Statistics;Optimization;Linear programming;Computational modeling;Evolutionary computation},
  doi={10.1109/CEC.2013.6557876},
  ISSN={1941-0026},
  month={June},
}

@misc{akhtar2019efficient,
  title={Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Search},
  author={Taimoor Akhtar and Christine A. Shoemaker},
  year={2019},
  eprint={1903.02167},
  archivePrefix={arXiv},
  primaryClass={cs.NE}
}

@article{LuST19,
  author    = {Xiaofen Lu and
      Tao Sun and
      Ke Tang},
  title     = {Evolutionary optimization with hierarchical surrogates},
  journal   = {Swarm Evol. Comput.},
  volume    = {47},
  pages     = {21--32},
  year      = {2019},
  url       = {https://doi.org/10.1016/j.swevo.2019.03.005},
  doi       = {10.1016/j.swevo.2019.03.005},
  timestamp = {Sat, 22 Feb 2020 19:26:28 +0100},
  biburl    = {https://dblp.org/rec/journals/swevo/LuST19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{nardi2019practical,
  title={Practical design space exploration},
  author={Nardi, Luigi and Koeplinger, David and Olukotun, Kunle},
  booktitle={2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)},
  pages={347--358},
  year={2019},
  organization={IEEE}
}


@misc{smac-2017,
  title={SMAC v3: Algorithm Configuration in Python},
  author={Marius Lindauer and Katharina Eggensperger and Matthias Feurer and Stefan Falkner and André Biedenkapp and Frank Hutter},
  year={2017},
  publisher={GitHub},
  howpublished={\url{https://github.com/automl/SMAC3}}
}

@inproceedings{HutterHL11,
  author    = {Frank Hutter and
      Holger H. Hoos and
      Kevin Leyton{-}Brown},
  title     = {Sequential Model-Based Optimization for General Algorithm Configuration},
  booktitle = {Learning and Intelligent Optimization - 5th International Conference,
      {LION} 5, Rome, Italy, January 17-21, 2011. Selected Papers},
  pages     = {507--523},
  year      = {2011},
  url       = {https://doi.org/10.1007/978-3-642-25566-3\_40},
  doi       = {10.1007/978-3-642-25566-3\_40},
  timestamp = {Sun, 02 Jun 2019 21:10:54 +0200},
  biburl    = {https://dblp.org/rec/conf/lion/HutterHL11.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{BischlmlrMBO,
  title = {{{mlrMBO}}: {{A Modular Framework}} for {{Model}}-{{Based Optimization}} of {{Expensive Black}}-{{Box Functions}}},
  url = {http://arxiv.org/abs/1703.03373},
  shorttitle = {{{mlrMBO}}},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1703.03373},
  primaryClass = {stat},
  author = {Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel},
  date = {2017-03-09},
}

@article{Knowles06,
  author    = {J. Knowles},
  title     = {ParEGO: a hybrid algorithm with on-line landscape approximation for
      expensive multiobjective optimization problems},
  journal   = {{IEEE} Trans. Evolutionary Computation},
  volume    = {10},
  number    = {1},
  pages     = {50--66},
  year      = {2006},
  url       = {https://doi.org/10.1109/TEVC.2005.851274},
  doi       = {10.1109/TEVC.2005.851274},
  timestamp = {Wed, 17 May 2017 14:25:39 +0200},
  biburl    = {https://dblp.org/rec/journals/tec/Knowles06.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{FalknerBOHB,
  author    = {Stefan Falkner and
      Aaron Klein and
      Frank Hutter},
  title     = {{BOHB:} Robust and Efficient Hyperparameter Optimization at Scale},
  journal   = {CoRR},
  volume    = {abs/1807.01774},
  year      = {2018},
  url       = {http://arxiv.org/abs/1807.01774},
  archivePrefix = {arXiv},
  eprint    = {1807.01774},
  timestamp = {Mon, 13 Aug 2018 16:47:48 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1807-01774.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Pukhkaiev19,
  author    = {Dmytro Pukhkaiev and
      Uwe A{\ss}mann},
  title     = {Parameter Tuning for Self-optimizing Software at Scale},
  journal   = {CoRR},
  volume    = {abs/1909.03814},
  year      = {2019},
  url       = {http://arxiv.org/abs/1909.03814},
  archivePrefix = {arXiv},
  eprint    = {1909.03814},
  timestamp = {Tue, 17 Sep 2019 11:23:44 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-03814.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Sobol1999,
  affiliation = {Institute of Mathematical Modelling of the Russian Academy of Sciences 4 Miusskaya SquareMoscow 125047, ⟨sobol#3⟩⟨levitan⟩@imamod.msk.suRussia},
  author = {Sobol, I. M. and Levitan, Yu. L.},
  journal = {Computers and Mathematics with Applications},
  keywords = {Random numbers; Pseudo-random numbers; Monte Carlo methods; Randomness tests; Multiple criteria decision making},
  language = {English},
  number = {4-5},
  pages = {33-40},
  title = {A Pseudo-Random Number Generator for Personal Computers},
  volume = {37},
  year = {1999},
}

@inproceedings{TobiasCV,
  author = {Wagner, Tobias},
  year = {2010},
  month = {01},
  pages = {1},
  title = {A Subjective Review of the State of the Art in Model-Based Parameter Tuning}
}

@book{HastieFT01,
  author    = {Trevor Hastie and
      Jerome H. Friedman and
      Robert Tibshirani},
  title     = {The Elements of Statistical Learning: Data Mining, Inference, and
      Prediction},
  series    = {Springer Series in Statistics},
  publisher = {Springer},
  year      = {2001},
  url       = {https://doi.org/10.1007/978-0-387-21606-5},
  doi       = {10.1007/978-0-387-21606-5},
  isbn      = {978-1-4899-0519-2},
  timestamp = {Mon, 22 Jul 2019 15:00:48 +0200},
  biburl    = {https://dblp.org/rec/books/sp/HastieFT01.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{TutMOEABrockhoff,
  author = {Brockhoff, Dimo and Wagner, Tobias},
  title = {Tutorial on Evolutionary Multiobjective Optimization},
  year = {2015},
  isbn = {9781450334884},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/2739482.2756574},
  doi = {10.1145/2739482.2756574},
  booktitle = {Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation},
  pages = {37–63},
  numpages = {27},
  location = {Madrid, Spain},
  series = {GECCO Companion ’15}
}

@article{ChughScal2019,
  author    = {Tinkle Chugh},
  title     = {Scalarizing Functions in Bayesian Multiobjective Optimization},
  journal   = {CoRR},
  volume    = {abs/1904.05760},
  year      = {2019},
  url       = {http://arxiv.org/abs/1904.05760},
  archivePrefix = {arXiv},
  eprint    = {1904.05760},
  timestamp = {Thu, 25 Apr 2019 13:55:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1904-05760.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Martens13,
  author    = {Marcus M{\"{a}}rtens and Dario Izzo},
  title     = {The asynchronous island model and {NSGA-II:} study of a new migration operator and its performance},
  booktitle = {Genetic and Evolutionary Computation Conference, {GECCO} '13, Amsterdam, The Netherlands, July 6-10, 2013},
  pages     = {1173--1180},
  year      = {2013},
  url       = {https://doi.org/10.1145/2463372.2463516},
  doi       = {10.1145/2463372.2463516},
  timestamp = {Tue, 06 Nov 2018 11:06:40 +0100},
  biburl    = {https://dblp.org/rec/conf/gecco/MartensI13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{NFL:DolpertM97,
  author    = {David H. Wolpert and
      William G. Macready},
  title     = {No free lunch theorems for optimization},
  journal   = {{IEEE} Trans. Evolutionary Computation},
  volume    = {1},
  number    = {1},
  pages     = {67--82},
  year      = {1997},
  url       = {https://doi.org/10.1109/4235.585893},
  doi       = {10.1109/4235.585893},
  timestamp = {Tue, 08 May 2018 14:46:40 +0200},
  biburl    = {https://dblp.org/rec/journals/tec/DolpertM97.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@book{Kaisa0021267,
  author    = {Kaisa Miettinen},
  title     = {Nonlinear multiobjective optimization},
  series    = {International series in operations research and management science},
  volume    = {12},
  publisher = {Kluwer},
  year      = {1998},
  isbn      = {978-0-7923-8278-2},
  timestamp = {Mon, 22 Jul 2019 16:40:55 +0200},
  biburl    = {https://dblp.org/rec/books/daglib/0021267.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{BeumeFLPV09,
  author    = {Nicola Beume and
  Carlos M. Fonseca and
  Manuel L{\'{o}}pez{-}Ib{\'{a}}{\~{n}}ez and
  Lu{\'{\i}}s Paquete and
  Jan Vahrenhold},
  title     = {On the Complexity of Computing the Hypervolume Indicator},
  journal   = {{IEEE} Trans. Evolutionary Computation},
  volume    = {13},
  number    = {5},
  pages     = {1075--1082},
  year      = {2009},
  url       = {https://doi.org/10.1109/TEVC.2009.2015575},
  doi       = {10.1109/TEVC.2009.2015575},
  timestamp = {Sun, 12 Nov 2017 23:10:48 +0100},
  biburl    = {https://dblp.org/rec/journals/tec/BeumeFLPV09.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DerbelBLV14,
author    = {Bilel Derbel and
Dimo Brockhoff and
Arnaud Liefooghe and
S{\'{e}}bastien V{\'{e}}rel},
title     = {On the Impact of Multiobjective Scalarizing Functions},
journal   = {CoRR},
volume    = {abs/1409.5752},
year      = {2014},
url       = {http://arxiv.org/abs/1409.5752},
archivePrefix = {arXiv},
eprint    = {1409.5752},
timestamp = {Mon, 13 Aug 2018 16:46:04 +0200},
biburl    = {https://dblp.org/rec/journals/corr/DerbelBLV14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


@MISC{Andersson00asurvey,
  author = {Johan Andersson},
  title = {A Survey of Multiobjective Optimization in Engineering Design Johan},
  year = {2000}
}

@article{WagEGOe,
  author = {Wagner, T.},
  title = {Planning and Multi-Objective Optimization of Manufacturing Processes by Means of Empirical Surrogate Models},
  journal   = {Vulkan Verlag},
  year = {2013}
}

@article{RamirezRV19,
  author    = {Aurora Ram{\'{\i}}rez and Jos{\'{e}} Ra{\'{u}}l Romero and Sebasti{\'{a}}n Ventura},
  title     = {A survey of many-objective optimisation in search-based software engineering},
  journal   = {J. Syst. Softw.},
  volume    = {149},
  pages     = {382--395},
  year      = {2019},
  url       = {https://doi.org/10.1016/j.jss.2018.12.015},
  doi       = {10.1016/j.jss.2018.12.015},
  timestamp = {Mon, 24 Feb 2020 15:58:16 +0100},
  biburl    = {https://dblp.org/rec/journals/jss/RamirezRV19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{KOURAKOS201313,
  title = "Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management",
  journal = "Journal of Hydrology",
  volume = "479",
  pages = "13 - 23",
  year = "2013",
  issn = "0022-1694",
  doi = "https://doi.org/10.1016/j.jhydrol.2012.10.050",
  url = "http://www.sciencedirect.com/science/article/pii/S0022169412009511",
  author = "George Kourakos and Aristotelis Mantoglou",
  keywords = "Salt water intrusion, Aquifer management, Coastal aquifers, Multi-objective optimization, Surrogate models, Pumping management",
  abstract = "Summary
  The demand for fresh water in coastal areas and islands can be very high due to increased local needs and tourism. A multi-objective optimization methodology is developed, involving minimization of economic and environmental costs while satisfying water demand. The methodology considers desalinization of pumped water and injection of treated water into the aquifer. Variable density aquifer models are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi-objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNNs)]. The surrogate models are trained adaptively during optimization based on a genetic algorithm. In the crossover step, each pair of parents generates a pool of offspring which are evaluated using the fast surrogate model. Then, the most promising offspring are evaluated using the exact numerical model. This procedure eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. The method has important advancements compared to previous methods such as precise evaluation of the Pareto set and alleviation of propagation of errors due to surrogate model approximations. The method is applied to an aquifer in the Greek island of Santorini. The results show that the new MOSA(MNN) algorithm offers significant reduction in computational time compared to previous methods (in the case study it requires only 5% of the time required by other methods). Further, the Pareto solution is better than the solution obtained by alternative algorithms."
}


@article{FigueiraA14,
  author    = {Gon{\c{c}}alo Figueira and Bernardo Almada{-}Lobo},
  title     = {Hybrid simulation-optimization methods: {A} taxonomy and discussion},
  journal   = {Simul. Model. Pract. Theory},
  volume    = {46},
  pages     = {118--134},
  year      = {2014},
  url       = {https://doi.org/10.1016/j.simpat.2014.03.007},
  doi       = {10.1016/j.simpat.2014.03.007},
  timestamp = {Mon, 24 Feb 2020 15:55:22 +0100},
  biburl    = {https://dblp.org/rec/journals/simpra/FigueiraA14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{JonesSW98,
  author    = {Donald R. Jones and
      Matthias Schonlau and
      William J. Welch},
  title     = {Efficient Global Optimization of Expensive Black-Box Functions},
  journal   = {J. Global Optimization},
  volume    = {13},
  number    = {4},
  pages     = {455--492},
  year      = {1998},
  url       = {https://doi.org/10.1023/A:1008306431147},
  doi       = {10.1023/A:1008306431147},
  timestamp = {Wed, 17 May 2017 14:25:32 +0200},
  biburl    = {https://dblp.org/rec/journals/jgo/JonesSW98.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{ZaeffererBNWE13,
  author    = {Martin Zaefferer and
      Thomas Bartz{-}Beielstein and
      Boris Naujoks and
      Tobias Wagner and
      Michael Emmerich},
  title     = {A Case Study on Multi-Criteria Optimization of an Event Detection
      Software under Limited Budgets},
  booktitle = {Evolutionary Multi-Criterion Optimization - 7th International Conference,
      {EMO} 2013, Sheffield, UK, March 19-22, 2013. Proceedings},
  pages     = {756--770},
  year      = {2013},
  url       = {https://doi.org/10.1007/978-3-642-37140-0\_56},
  doi       = {10.1007/978-3-642-37140-0\_56},
  timestamp = {Tue, 14 May 2019 10:00:47 +0200},
  biburl    = {https://dblp.org/rec/conf/emo/ZaeffererBNWE13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{inproceedings,
  author = {Ponweiser, Wolfgang and Wagner, Tobias and Biermann, Dirk and Vincze, Markus},
  year = {2008},
  month = {09},
  pages = {784-794},
  title = {Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted $\mathcal{S}$ -Metric Selection},
  doi = {10.1007/978-3-540-87700-4_78}
}


@inproceedings{WilsonHSX16,
  author    = {Andrew Gordon Wilson and
      Zhiting Hu and
      Ruslan Salakhutdinov and
      Eric P. Xing},
  title     = {Deep Kernel Learning},
  booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence
      and Statistics, {AISTATS} 2016, Cadiz, Spain, May 9-11, 2016},
  pages     = {370--378},
  year      = {2016},
  url       = {http://proceedings.mlr.press/v51/wilson16.html},
  timestamp = {Wed, 29 May 2019 08:41:44 +0200},
  biburl    = {https://dblp.org/rec/conf/aistats/WilsonHSX16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Deb99,
  author    = {Kalyanmoy Deb},
  title     = {Multi-objective Genetic Algorithms: Problem Difficulties and Construction
      of Test Problems},
  journal   = {Evol. Comput.},
  volume    = {7},
  number    = {3},
  pages     = {205--230},
  year      = {1999},
  url       = {https://doi.org/10.1162/evco.1999.7.3.205},
  doi       = {10.1162/evco.1999.7.3.205},
  timestamp = {Fri, 13 Mar 2020 14:35:23 +0100},
  biburl    = {https://dblp.org/rec/journals/ec/Deb99.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DebAPM00,
author    = {Kalyanmoy Deb and
      Samir Agrawal and
      Amrit Pratap and
      T. Meyarivan},
  title     = {A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective
  Optimisation: {NSGA-II}},
  booktitle = {Parallel Problem Solving from Nature - {PPSN} VI, 6th International
  Conference, Paris, France, September 18-20, 2000, Proceedings},
  pages     = {849--858},
  year      = {2000},
  url       = {https://doi.org/10.1007/3-540-45356-3\_83},
  doi       = {10.1007/3-540-45356-3\_83},
  timestamp = {Tue, 14 May 2019 10:00:53 +0200},
  biburl    = {https://dblp.org/rec/conf/ppsn/DebAPM00.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{bookGOF,
  author = {Gamma, Erich and Helm, Richard and Johnson, Ralph and Vlissides, John},
  title = {Design Patterns: Elements of Reusable Object-Oriented Software},
  year = {1995},
  isbn = {0201633612},
  publisher = {Addison-Wesley Longman Publishing Co., Inc.},
  address = {USA}
}

@inproceedings{SpringenbergKFH16,
  author    = {Jost Tobias Springenberg and
      Aaron Klein and
      Stefan Falkner and
      Frank Hutter},
  title     = {Bayesian Optimization with Robust Bayesian Neural Networks},
  booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference
      on Neural Information Processing Systems 2016, December 5-10, 2016,
      Barcelona, Spain},
  pages     = {4134--4142},
  year      = {2016},
  url       = {http://papers.nips.cc/paper/6117-bayesian-optimization-with-robust-bayesian-neural-networks},
  timestamp = {Fri, 06 Mar 2020 17:00:15 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/SpringenbergKFH16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}