Journal Articles 2020(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. , "An analysis of the hypervolume Sharpe-ratio indicator", European Journal of Operational Research, vol. 283, pp. 614-629, 2020 2018(2 publications) [publication]Andreia P. Guerreiro and Fonseca, C.M. , "Computing and updating hypervolume contributions in up to four dimensions", IEEE Transactions on Evolutionary Computation., vol. 22, pp. 449-463, 2018 [publication]Gomes, R.J.P. and Andreia P. Guerreiro and Kuhn, T. and Paquete, L. , "Implicit enumeration strategies for the hypervolume subset selection problem", Computers & Operations Research, vol. 100, pp. 244-253, 2018 2016(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. and Paquete, L. , "Greedy Hypervolume Subset Selection in Low Dimensions", Evolutionary Computation, vol. 24, 2016 Conference Articles 2016(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. , "Hypervolume Sharpe-ratio indicator: Formalization and first theoretical results", in Parallel Problem Solving from Nature - PPSN XIV, vol. 9921 of Lecture Notes in Computer Science, pp. 814-823, Springer, 2016 2015(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. and Paquete, L. , " Greedy hypervolume subset selection in the three-objective case ", in Proceedings of the 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), pp 671-678 , 2015 [citation][year=2015]T Friedrich, F Neumann Maximizing submodular functions under matroid constraints by evolutionary algorithms Evolutionary computation, 2015 2014(1 publication) [publication]Yevseyeva, I. and Andreia P. Guerreiro and Emmerich, M.T.M. and Fonseca, C.M. , "A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms", in Parallel Problem Solving from Nature – PPSN XIII, 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, LNCS 8672, 2014 [citation][year=2016]Ahmadi, Aras. "Memory-based Adaptive Partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms." Applied Soft Computing (2016). [citation][year=2015]Souravlias, D., K. E. Parsopoulos, and I. S. Kotsireas. "Circulant weighing matrices: a demanding challenge for parallel optimization metaheuristics." Optimization Letters (2015): 1-12. 2012(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. and Emmerich, M.T.M. , "A fast dimension-sweep algorithm for the hypervolume indicator in four dimensions", in 24th Canadian Conference on Computational Geometry (CCCG 2012), 2012 [citation][year=2015]Yang, Zhiwei, et al. "Multicriteria Inventory Routing by Cooperative Swarms and Evolutionary Algorithms." Bioinspired Computation in Artificial Systems. Springer International Publishing, 2015. 127-137. [citation][year=2015]Siwei Jiang; Jie Zhang; Yew-Soon Ong; Zhang, A.N.; Puay Siew Tan, "A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm," in Cybernetics, IEEE Transactions on , vol.45, no.10, pp.2202-2213, Oct. 2015 doi: 10.1109/TCYB.2014.2367526 [citation][year=2014]Russo, L.M.S.and; Francisco, A.P., "Quick Hypervolume," IEEE Transactions on Evolutionary Computation, Volume 18, Issue 4, pp. 481 - 502, 2014. http://dx.doi.org/10.1109/TEVC.2013.2281525 [citation][year=2014]M. Emmerich and A. Deutz, “Time complexity and zeros of the hypervolume indicator gradient field,” in EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III (O. Schuetze, C. A. Coello Coello, A.-A. Tantar, E. Tantar, P. Bouvry, P. Del Moral, P. Legrand, eds.), vol. 500 of Studies in Computational Intelligence, pp 169-193, Berlin: Springer, 2014. [citation][year=2014]Siwei Jiang, Jie Zhang, Yew-Soon Ong, Allan N. Zhang, and Puay Siew Tan, "A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm", IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2014.2367526, Published online 2 December 2014. [citation][year=2014]Michael T.M. Emmerich, André H. Deutz, Iryna Yevseyeva, "On Reference Point Free Weighted Hypervolume Indicators based on Desirability Functions and their Probabilistic Interpretation", Procedia Technology, Volume 16, Pages 532–541, 2014. [citation][year=2014]Krzysztof Nowak, Marcus Märtens, Dario Izzo, "Empirical Performance of the Approximation of the Least Hypervolume Contributor", Parallel Problem Solving from Nature – PPSN XIII, Lecture Notes in Computer Science Volume 8672, pp 662-671, 2014. [citation][year=2013]K. Bringmann, “Bringing Order to Special Cases of Klee’s Measure Problem,” in Mathematical Foundations of Computer Science 2013 (K. Chatterjee and J. Sgall, eds.), vol. 8087 of Lecture Notes in Computer Science, pp 207-218, Berlin: Springer, 2013. [citation][year=2013]I. Hupkens and M. Emmerich , “Logarithmic-time updates in SMS-EMOA and hypervolume-based archiving,” in EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV ( M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, E. Tantar, A.-A. Tantar, P. Del Moral, P. Legrand, P. Bouvry, C. A. Coello, eds.), vol. 227 of Advances in Intelligent Systems and Computing, pp 155-169 Berlin: Springer, 2013. [citation][year=2013]A. Zhou, Q. Zhang, and G. Zhang, “Approximation Model Guided Selection for Evolutionary Multiobjective Optimization,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp 398-412, Berlin: Springer, 2013. [citation][year=2013]M. Emmerich, A. Deutz, J. Kruisselbrink, and P. K. Shukla , “Cone-Based Hypervolume Indicators: Construction, Properties, and Efficient Computation,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp 111-127, Berlin: Springer, 2013. [citation][year=2013]P. K. Shukla and M. A. Braun, “Indicator Based Search in Variable Orderings: Theory and Algorithms,” in Evolutionary Multi-Criterion Optimization. 7th International Conference, EMO 2013 (R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, eds.), vol. 7811 of Lecture Notes in Computer Science, pp 66-80, Berlin: Springer, 2013. [citation][year=2013]João A. Duro, Machine learning based decision support for a class of many-objective optimisation problems, Ph.D. Thesis, Cranfield University, 2013. 2011(1 publication) [publication]Fonseca, C.M. and Andreia P. Guerreiro and López-Ibáñez, M. and Paquete, L. , "On the Computation of the Empirical Attainment Function", in Sixth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2011), LNCS 6576, 106-190, Springer, 2011 [citation][year=2015]JP Martins, Análise da aprendizagem de ligações em otimização evolutiva, Tese de Doutorado, Instituto de Ciências Matemáticas e de Computação, USP São Carlos, 2015. [citation][year=2015]Ioannis Tsoukalas, Christos Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling & Software, Volume 69, July 2015, Pages 396-413, ISSN 1364-8152, http://dx.doi.org/10.1016/j.envsoft.2014.09.023. [citation][year=2015]WP Tsai, FJ Chang, LC Chang, EE Herricks AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands Journal of Hydrology, 2015 [citation][year=2015]L Yliniemi, D Wilson, K Tumer Multi-Objective Multiagent Credit Assignment in NSGA-II Using Difference Evaluations AAMAS '15 Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems Pages 1635-1636 [citation][year=2015]LM Yliniemi Multi-Objective Optimization in Multiagent Systems PhD thesis, Oregon State University [citation][year=2014]T Tušar, B Filipic, Initial experiments in visualization of empirical attainment function differences using maximum intensity projection,Proceedings of the 2014 conference on Genetic and evolutionary computation companion, Pages 1099-1106, 2014 [citation][year=2014]I Tsoukalas, C Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling & Software, 2014 [citation][year=2014]T Tušar, B Filipic, Visualizing Exact and Approximated 3D Empirical Attainment Functions, Mathematical Problems in Engineering, 2014 [citation][year=2014]L Yliniemi, K Tumer, PaCcET: An Objective Space Transformation to Iteratively Convexify the Pareto Front , Simulated Evolution and Learning, 2014 [citation][year=2014]FMO no Guzmán, IR Ruiz, Development of Advanced Computational Systems for Multiple Sequence Alignments by using Heterogeneous Biological Information, Doctoral Thesis - Francisco M Ortuno Guzman, 2014 [citation][year=2013]T. Tušar and B. Filipic, "An approach to visualizing the 3D empirical attainment function," in GECCO '13 Companion Proceedings, Fifteenth Annual Conference on Genetic and Evolutionary Computation (C. Blum, ed.), pp. 1367-1372, 2013. [citation][year=2012]Michael P. Cipold, Pradyumn Kumar Shukla, Claus C. Bachmann, Kaibin Bao, Hartmut Schmeck , An Evolutionary Optimization Approach for Bulk Material Blending Systems, Parallel Problem Solving from Nature - PPSN XII [citation][year=2012]D Brockhoff, K Deb, GECCO 2012 tutorial on evolutionary multiobjective optimization, Proceedings of the fourteenth international conference on Genetic and evolutionary computation Tech Report 2017(1 publication) [publication]Andreia P. Guerreiro and Fonseca, C.M. , "Computing and updating hypervolume contributions in up to four dimensions", 2017