Publications (since 2019)

Edited books

L. Paquete and C. Zarges, ed. (2020). Evolutionary Computation in Combinatorial Optimization - 20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings. Vol. 12102. Lecture Notes in Computer Science. Springer. DOI: 10.1007/978-3-030-43680-3.

A. Liefooghe and L. Paquete, ed. (2019). Evolutionary Computation in Combinatorial Optimization - 19th European Conference, EvoCOP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24-26, 2019, Proceedings. Vol. 11452. Lecture Notes in Computer Science. Springer. DOI: 10.1007/978-3-030-16711-0.

Journal articles

F. Clément, C. Doerr, and L. Paquete (2022). “Star discrepancy subset selection: Problem formulation and efficient approaches for low dimensions”. In: Journal of Complexity 70, p. 101645. DOI: https://doi.org/10.1016/j.jco.2022.101645.

N. Godinho, H. Silva, M. Curado, and L. Paquete (2022). “A reconfigurable resource management framework for fog environments”. In: Future Generation Computer Systems 133, pp. 124-140. DOI: https://doi.org/10.1016/j.future.2022.03.015.

C. Gomes, G. Falcão, L. Paquete, and J. P. Fernandes (2022). “An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization”. In: SN Computer Science 3, p. 335. DOI: https://doi.org/10.1007/s42979-022-01215-9.

L. Paquete, B. Schulze, M. Stiglmayr, and A. C. Lourenço (2022). “Computing Representations using Hypervolume Scalarizations”. In: Computers and Operations Research, p. 105349. DOI: https://doi.org/10.1016/j.cor.2021.105349.

P. Correia, L. Paquete, and J. R. Figueira (2021). “Finding Multi-objective Supported Efficient Spanning Trees”. In: Computational Optimization and Applications 78.2. Open access, pp. 491-528. DOI: https://doi.org/10.1007/s10589-020-00251-6.

A. P. Guerreiro, C. M. Fonseca, and L. Paquete (2021). “The Hypervolume Indicator: Computational Problems and Algorithms”. In: ACM Computing Surveys 56.1. Open access, pp. 1-42. DOI: https://doi.org/10.1145/3453474. URL: https://arxiv.org/abs/2005.00515.

M. López-Ibáñez, J. Branke, and L. Paquete (2021). “Reproducibility in Evolutionary Computation”. In: ACM Transactions on Evolutionary Learning and Optimization 1.4. Open access, pp. 1-21. URL: https://doi.org/10.1145/3466624.

M. Stiglmayr, J. R. Figueira, K. Klamroth, L. Paquete, and B. Schulze (2021). “Decision Space Robustness for Multi-Objective Integer Linear Programming”. In: Annals of Operations Research. DOI: https://doi.org/10.1007/s10479-021-04462-w.

D. Abreu, K. Castro, L. Paquete, M. Curado, and E. Monteiro (2020). “Resilient Service Chains Through Smart Replication”. In: IEEE Access. Open access, pp. 187021 - 187036. DOI: 10.1109/ACCESS.2020.3030537.

A. P. Guerreiro and C. M. Fonseca (2020). “An Analysis of the Hypervolume Sharpe-Ratio Indicator”. In: European Journal of Operational Research 283.2. Open access, pp. 614-629. DOI: 10.1016/j.ejor.2019.11.023.

A. Jesus, L. Paquete, and A. Liefooghe (2020). “A Model of Anytime Algorithm Performance for Bi-Objective Optimization”. In: Journal of Global Optimization. Open access. DOI: 10.1007/s10898-020-00909-9.

P. S. Oliveto, A. Auger, F. Chicano, and C. M. Fonseca (2020). “Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation”. In: IEEE Transactions on Evolutionary Computation 24.6, pp. 993-994. DOI: 10.1109/TEVC.2020.3035225.

B. Schulze, M. Stiglmayr, L. Paquete, C. M. Fonseca, D. Willems, and S. Ruzika (2020). “On the Rectangular Knapsack Problem: Approximation of a Specific Quadratic Knapsack Problem”. In: Mathematical Methods of Operations Research. Open access, pp. 107-132. DOI: 10.1007/s00186-020-00702-0.

Conference articles

D. M. Dias, A. D. Jesus, and L. Paquete (2021). “A software library for archiving nondominated points”. In: GECCO ‘21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021. Ed. by K. Krawiec. ACM, pp. 53-54. DOI: 10.1145/3449726.3462737.

A. D. Jesus, L. Paquete, B. Derbel, and A. Liefooghe (2021). “On the design and anytime performance of indicator-based branch and bound for multi-objective combinatorial optimization”. In: GECCO ‘21: Genetic and Evolutionary Computation Conference, Lille, France, July 10-14, 2021. Ed. by F. Chicano and K. Krawiec. ACM, pp. 234-242. DOI: 10.1145/3449639.3459360.

L. Paquete and M. López-Ibáñez (2021). “Replicability and reproducibility in evolutionary optimization”. In: GECCO ‘21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021. Ed. by K. Krawiec. ACM, pp. 454-462. DOI: 10.1145/3449726.3461405.

N. Godinho, H. Silva, M. Curado, and L. Paquete (2020). “Energy and Latency-aware Resource Reconfiguration in Fog Environments”. In: 2019 IEEE Symposium on Network Computing and Applications, NCA 2020, November 24-27, 2020.

A. D. Jesus, A. Liefooghe, B. Derbel, and L. Paquete (2020). “Algorithm Selection of Anytime Algorithms”. In: GECCO ‘20: Genetic and Evolutionary Computation Conference, Cancún Mexico, July 8-12, 2020. Ed. by C. A. C. Coello. ACM, pp. 850-858. DOI: 10.1145/3377930.3390185.

K. Velasquez, D. P. Abreu, L. Paquete, M. Curado, and E. Monteiro (2020). “A Rank-based Mechanism for Service Placement in the Fog”. In: 2020 IFIP Networking Conference, Networking 2020, Paris, France, June 22-26, 2020. IEEE, pp. 64-72.

N. Godinho, M. Curado, and L. Paquete (2019). “Optimization of Service Placement with Fairness”. In: 2019 IEEE Symposium on Computers and Communications, ISCC 2019, Barcelona, Spain, June 29 - July 3, 2019. IEEE, pp. 1-6. DOI: 10.1109/ISCC47284.2019.8969652.

N. Godinho and L. Paquete (2019). “A Combinatorial Branch and Bound for the Min-Max Regret Spanning Tree Problem”. In: Analysis of Experimental Algorithms - Special Event, SEA(^2) 2019, Kalamata, Greece, June 24-29, 2019, Revised Selected Papers. Ed. by I. S. Kotsireas, P. M. Pardalos, K. E. Parsopoulos, D. Souravlias and A. Tsokas. Vol. 11544. Lecture Notes in Computer Science. Springer, pp. 69-81. DOI: 10.1007/978-3-030-34029-2_5.

G. Moreira and L. Paquete (2019). “Guiding under uniformity measure in the decision space”. In: IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019, Guayaquil, Ecuador, November 11-15, 2019. IEEE, pp. 1-6. DOI: 10.1109/LA-CCI47412.2019.9037034.

L. E. Schäfer, T. Dietz, M. V. Natale, S. Ruzika, S. O. Krumke, and C. M. Fonseca (2019). “The Bicriterion Maximum Flow Network Interdiction Problem in s-t-Planar Graphs”. In: Operations Research Proceedings 2019, Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Dresden, Germany, September 4-6, 2019. Ed. by J. S. Neufeld, U. Buscher, R. Lasch, D. Möst and J. Schönberger. Springer, pp. 133-139. DOI: 10.1007/978-3-030-48439-2_16.

Other

C. Doerr and L. Paquete (2021). “Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions”. In: CoRR abs/2101.07881. eprint: 2101.07881. URL: https://arxiv.org/abs/2101.07881.

C. Doerr, C. M. Fonseca, T. Friedrich, and X. Yao (2020). “Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431)”. In: Dagstuhl Reports 9.10. Ed. by C. Doerr, C. M. Fonseca, T. Friedrich and X. Yao, pp. 61-94. ISSN: 2192-5283. DOI: 10.4230/DagRep.9.10.61. URL: https://drops.dagstuhl.de/opus/volltexte/2020/11856.

C. M. Fonseca, K. Klamroth, G. Rudolph, and M. M. Wiecek (2020). “Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)”. In: Dagstuhl Reports 10.1. Ed. by C. M. Fonseca, K. Klamroth, G. Rudolph and M. M. Wiecek, pp. 52-129. ISSN: 2192-5283. DOI: 10.4230/DagRep.10.1.52. URL: https://drops.dagstuhl.de/opus/volltexte/2020/12401.

L. E. Schäfer, S. Ruzika, S. O. Krumke, and C. M. Fonseca (2020). “On the Bicriterion Maximum Flow Network Interdiction Problem”. In: CoRR abs/2010.02730. eprint: 2010.02730. URL: https://arxiv.org/abs/2010.02730.