Skip to content

References

These are some references we used for this work:

  • J. Blank and K. Deb, pymoo: Multi-objective Optimization in Python, IEEE Access, 2020, 1-1

  • ECJ then and now, Luke, Sean, Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1223--1230, 2017

  • PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], Tian, Ye and Cheng, Ran and Zhang, Xingyi and Jin, Yaochu, IEEE Computational Intelligence Magazine, 12, 4, 73--87, 2017, IEEE

  • PISA---a platform and programming language independent interface for search algorithms, Bleuler, Stefan and Laumanns, Marco and Thiele, Lothar and Zitzler, Eckart, International Conference on Evolutionary Multi-Criterion Optimization, 494--508, 2003, Springer

  • ParadisEO-MOEO: A framework for evolutionary multi-objective optimization, Liefooghe, Arnaud and Basseur, Matthieu and Jourdan, Laetitia and Talbi, El-Ghazali, International Conference on Evolutionary Multi-Criterion Optimization, 386--400, 2007, Springer

  • Opt4J: a modular framework for meta-heuristic optimization, Lukasiewycz, Martin and Glass=tex, Michael and Reimann, Felix and Teich, Jurgen, Proceedings of the 13th annual conference on Genetic and evolutionary computation, 1723--1730, 2011

  • MOEA framework: a free and open source java framework for multiobjective optimization, Hadka, David, 2012, Version

  • Redesigning the jMetal multi-objective optimization framework, Nebro, Antonio J and Durillo, Juan J and Vergne, Matthieu, Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation, 1093--1100, 2015

  • jMetal: A Java framework for multi-objective optimization, Durillo, Juan J and Nebro, Antonio J, Advances in Engineering Software, 42, 10, 760--771, 2011, Elsevier

  • An extensible JCLEC-based solution for the implementation of multi-objective evolutionary algorithms, Ramirez, Aurora and Romero, Jose Raul and Ventura, Sebastian, Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 1085--1092, 2015

  • EvA: a tool for optimization with evolutionary algorithms, Wakunda, Jurgen and Zell, Andreas, EUROMICRO 97. Proceedings of the 23rd EUROMICRO Conference: New Frontiers of Information Technology (Cat. No. 97TB100167), 644--651, 1997, IEEE

  • Interpersonal Comparisons of the Good: Epistemic not Impossible, Coakley, Mathew, Utilitas, 28, 3, 288--313, 2016, Cambridge University Press

  • Multiobjective genetic algorithms, Fonseca, Carlos M and Fleming, Peter J, IEE colloquium on genetic algorithms for control systems engineering, 6--1, 1993, IET

  • The efficiency theorems and market failure, Hammond, P, Elements of General Equilibrium Analysis, 211--260, 1998, Oxford: Blackwell

  • MCDM---if not a roman numeral, then what?, Zionts, Stanley, Interfaces, 9, 4, 94--101, 1979, INFORMS

  • Performance indicators in multiobjective optimization, Audet, Charles and Bigeon, J and Cartier, D and Le Digabel, Sebastien and Salomon, Ludovic, Optimization Online, 2018

  • Output-sensitive peeling of convex and maximal layers, Nielsen, Franck, Information processing letters, 59, 5, 255--259, 1996, Elsevier

  • On finding the maxima of a set of vectors, Kung, Hsiang-Tsung and Luccio, Fabrizio and Preparata, Franco P, Journal of the ACM (JACM), 22, 4, 469--476, 1975, ACM New York, NY, USA

  • Algorithms and analyses for maximal vector computation, Godfrey, Parke and Shipley, Ryan and Gryz, Jarek, The VLDB Journal, 16, 1, 5--28, 2007, Springer

  • Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation, Kim, Il Yong and De Weck, OL, Structural and multidisciplinary optimization, 31, 2, 105--116, 2006, Springer

  • On a bicriterion formation of the problems of integrated system identification and system optimization, YV, YV HAIMES and Lasdon, Leon S and Da Wismer, DA, IEEE Transactions on Systems, Man and Cybernetics, 3, 296--297, 1971, Institute of Electrical and Electronics Engineers Inc.

  • Effective implementation of the \(\varepsilon\)-constraint method in multi-objective mathematical programming problems, Mavrotas, George, Applied mathematics and computation, 213, 2, 455--465, 2009, Elsevier

  • Deductive sort and climbing sort: New methods for non-dominated sorting, McClymont, Kent and Keedwell, Ed, Evolutionary computation, 20, 1, 1--26, 2012, MIT Press

  • An efficient non-dominated sorting method for evolutionary algorithms, Fang, Hongbing and Wang, Qian and Tu, Yi-Cheng and Horstemeyer, Mark F, Evolutionary computation, 16, 3, 355--384, 2008, MIT Press

  • Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms, Jensen, Mikkel T, IEEE Transactions on Evolutionary Computation, 7, 5, 503--515, 2003, IEEE

  • An efficient approach to nondominated sorting for evolutionary multiobjective optimization, Zhang, Xingyi and Tian, Ye and Cheng, Ran and Jin, Yaochu, IEEE Transactions on Evolutionary Computation, 19, 2, 201--213, 2014, IEEE

  • Comparison of data structures for storing Pareto-sets in MOEAs, Mostaghim, Sanaz and Teich, Jurgen and Tyagi, Ambrish, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600), 1, 843--848, 2002, IEEE

  • A fast incremental BSP tree archive for non-dominated points, Glasmachers, Tobias, International Conference on Evolutionary Multi-Criterion Optimization, 252--266, 2017, Springer

  • Data structures in multi-objective evolutionary algorithms, Altwaijry, Najwa and Menai, Mohamed El Bachir, Journal of Computer Science and Technology, 27, 6, 1197--1210, 2012, Springer

  • An efficient approach to unbounded bi-objective archives- introducing the mak_tree algorithm, Berry, Adam and Vamplew, Peter, Proceedings of the 8th annual conference on Genetic and evolutionary computation, 619--626, 2006

  • ND-tree-based update: a fast algorithm for the dynamic nondominance problem, Jaszkiewicz, Andrzej and Lust, Thibaut, IEEE Transactions on Evolutionary Computation, 22, 5, 778--791, 2018, IEEE @articlealberto2004representation, Representation and management of MOEA populations based on graphs, Alberto, Isolina and Mateo, Pedro M, European Journal of Operational Research, 159, 1, 52--65, 2004, Elsevier A dominance tree and its application in evolutionary multi-objective optimization, Shi, Chuan and Yan, Zhenyu and Lu, Kevin and Shi, Zhongzhi and Wang, Bai, Information Sciences, 179, 20, 3540--3560, 2009, Elsevier

  • Using unconstrained elite archives for multiobjective optimization, Fieldsend, Jonathan T and Everson, Richard M and Singh, Sameer, IEEE Transactions on Evolutionary Computation, 7, 3, 305--323, 2003, IEEE

  • A fast multi-objective evolutionary algorithm based on a tree structure, Shi, Chuan and Yan, Zhenyu and Shi, Zhongzhi and Zhang, Lei, Applied Soft Computing, 10, 2, 468--480, 2010, Elsevier

  • InterQuad: An interactive quad tree based procedure for solving the discrete alternative multiple criteria problem, Sun, Minghe and Steuer, Ralph T, European Journal of Operational Research, 89, 3, 462--472, 1996, Elsevier

  • Quad trees, a datastructures for discrete vector optimization problems, Habenicht, Walter, Essays and Surveys on Multiple Criteria Decision Making, 136--145, 1983, Springer

  • Full elite sets for multi-objective optimisation, Everson, Richard M and Fieldsend, Jonathan T and Singh, Sameer, Adaptive Computing in Design and Manufacture V, 343--354, 2002, Springer

  • Priority search trees, McCreight, Edward M, SIAM Journal on Computing, 14, 2, 257--276, 1985, SIAM

  • Data structures for range searching, Bentley, Jon Louis and Friedman, Jerome H, ACM Computing Surveys (CSUR), 11, 4, 397--409, 1979, ACM New York, NY, USA

  • Multidimensional binary search trees used for associative searching, Bentley, Jon Louis, Communications of the ACM, 18, 9, 509--517, 1975, ACM New York, NY, USA

  • The art of Unix programming, Raymond, Eric S, 2003, Addison-Wesley Professional

  • Quad-trees and linear lists for identifying nondominated criterion vectors, Sun, Minghe and Steuer, Ralph T, INFORMS Journal on Computing, 8, 4, 367--375, 1996, INFORMS

  • A new data structure for the nondominance problem in multi-objective optimization, Schutze, Oliver, International Conference on Evolutionary Multi-Criterion Optimization, 509--518, 2003, Springer

  • The legacy of modern portfolio theory, Fabozzi, Frank J and Gupta, Francis and Markowitz, Harry M, The Journal of Investing, 11, 3, 7--22, 2002, Institutional Investor Journals Umbrella

  • Tensorflow: A system for large-scale machine learning, Abadi, Marti=texn and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others, 12th \(\$USENIX\)$ Symposium on Operating Systems Design and Implementation (\(\$OSDI\)$ 16), 265--283, 2016

  • Quad trees a data structure for retrieval on composite keys, Finkel, Raphael A. and Bentley, Jon Louis, Acta informatica, 4, 1, 1--9, 1974, Springer

  • Multidimensional binary search trees used for associative searching, Bentley, Jon Louis, Communications of the ACM, 18, 9, 509--517, 1975, ACM New York, NY, USA

  • Geometric modeling using octree encoding, Meagher, Donald, Computer graphics and image processing, 19, 2, 129--147, 1982, Elsevier

  • The R*-tree: an efficient and robust access method for points and rectangles, Beckmann, Norbert and Kriegel, Hans-Peter and Schneider, Ralf and Seeger, Bernhard, Proceedings of the 1990 ACM SIGMOD international conference on Management of data, 322--331, 1990

  • R-trees: A dynamic index structure for spatial searching, Guttman, Antonin, Proceedings of the 1984 ACM SIGMOD international conference on Management of data, 47--57, 1984

  • The X-tree: An index structure for high-dimensional data, Berchtold, Stefan and Keim, Daniel A and Kriegel, Hans-Peter, Very Large Data-Bases, 28--39, 1996

  • Quadboost: A scalable concurrent quadtree, Zhou, Keren and Tan, Guangming and Zhou, Wei, IEEE Transactions on Parallel and Distributed Systems, 29, 3, 673--686, 2017, IEEE

  • Distance browsing in spatial databases, Hjaltason, Gi=texsli R and Samet, Hanan, ACM Transactions on Database Systems (TODS), 24, 2, 265--318, 1999, ACM New York, NY, USA

  • An improved dimension-sweep algorithm for the hypervolume indicator, Fonseca, Carlos M and Paquete, Lui=texs and Lopez-Ibanez, Manuel, 2006 IEEE international conference on evolutionary computation, 1157--1163, 2006, IEEE

  • Speeding up many-objective optimization by Monte Carlo approximations, Bringmann, Karl and Friedrich, Tobias and Igel, Christian and Voss=tex, Thomas, Artificial Intelligence, 204, 22--29, 2013, Elsevier

  • On the complexity of computing the hypervolume indicator, Beume, Nicola and Fonseca, Carlos M and Lopez-Ibanez, Manuel and Paquete, Lui=texs and Vahrenhold, Jan, IEEE Transactions on Evolutionary Computation, 13, 5, 1075--1082, 2009, IEEE

  • Aggregation trees for visualization and dimension reduction in many-objective optimization, de Freitas, Alan RR and Fleming, Peter J and Guimaraes, Frederico G, Information Sciences, 298, 288--314, 2015, Elsevier

  • Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems, Ishibuchi, Hisao and Masuda, Hiroyuki and Tanigaki, Yuki and Nojima, Yusuke, 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), 170--177, 2014, IEEE