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Carlos M. Fonseca
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- affiliation: University of Coimbra, Portugal
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2020 – today
- 2024
- [j22]Carlos Henggeler Antunes, Carlos M. Fonseca, Luís Paquete, Michael Stiglmayr:
Special issue on exact and approximation methods for mixed-integer multi-objective optimization. Math. Methods Oper. Res. 100(1): 1-4 (2024) - 2023
- [p4]Andreia P. Guerreiro, Kathrin Klamroth
, Carlos M. Fonseca:
Theoretical Aspects of Subset Selection in Multi-Objective Optimisation. Many-Criteria Optimization and Decision Analysis 2023: 213-239 - [i10]Richard Allmendinger, Carlos M. Fonseca, Serpil Sayin, Margaret M. Wiecek, Michael Stiglmayr:
Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361). Dagstuhl Reports 13(9): 1-68 (2023) - 2022
- [j21]Andreia P. Guerreiro, Carlos M. Fonseca, Luís Paquete
The Hypervolume Indicator: Computational Problems and Algorithms. ACM Comput. Surv. 54(6): 119:1-119:42 (2022) - [i9]Anne Auger, Carlos M. Fonseca, Tobias Friedrich, Johannes Lengler:
Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081). Dagstuhl Reports 12(2): 87-102 (2022) - 2021
- [j20]Joaquim Leitão
A Compressive Receding Horizon Approach for Smart Home Energy Management. IEEE Access 9: 100407-100435 (2021) - 2020
- [j19]Andreia P. Guerreiro
An analysis of the Hypervolume Sharpe-Ratio Indicator. Eur. J. Oper. Res. 283(2): 614-629 (2020) - [j18]Britta Schulze
On the rectangular knapsack problem: approximation of a specific quadratic knapsack problem. Math. Methods Oper. Res. 92(1): 107-132 (2020) - [j17]Pietro S. Oliveto
Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation. IEEE Trans. Evol. Comput. 24(6): 993-994 (2020) - [i8]Andreia P. Guerreiro, Carlos M. Fonseca, Luís Paquete:
The Hypervolume Indicator: Problems and Algorithms. CoRR abs/2005.00515 (2020) - [i7]Luca E. Schäfer, Stefan Ruzika, Sven O. Krumke, Carlos M. Fonseca:
On the Bicriterion Maximum Flow Network Interdiction Problem. CoRR abs/2010.02730 (2020) - [i6]Carlos M. Fonseca, Kathrin Klamroth
Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031). Dagstuhl Reports 10(1): 52-129 (2020)
2010 – 2019
- 2019
- [c40]Luca E. Schäfer
The Bicriterion Maximum Flow Network Interdiction Problem in s-t-Planar Graphs. OR 2019: 133-139 - [i5]Carola Doerr
Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431). Dagstuhl Reports 9(10): 61-94 (2019) - 2018
- [j16]André Riker
Energy-efficient multigroup communication. Trans. Emerg. Telecommun. Technol. 29(3) (2018) - [j15]Andreia P. Guerreiro
Computing and Updating Hypervolume Contributions in Up to Four Dimensions. IEEE Trans. Evol. Comput. 22(3): 449-463 (2018) - [c39]João Macedo
Geometric Crossover in Syntactic Space. EuroGP 2018: 237-252 - [e5]Anne Auger, Carlos M. Fonseca, Nuno Lourenço, Penousal Machado, Luís Paquete, L. Darrell Whitley:
Parallel Problem Solving from Nature - PPSN XV - 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part I. Lecture Notes in Computer Science 11101, Springer 2018, ISBN 978-3-319-99252-5 [contents] - [e4]Anne Auger, Carlos M. Fonseca
Parallel Problem Solving from Nature - PPSN XV - 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part II. Lecture Notes in Computer Science 11102, Springer 2018, ISBN 978-3-319-99258-7 [contents] - 2017
- [j14]Renaud Lacour, Kathrin Klamroth
A box decomposition algorithm to compute the hypervolume indicator. Comput. Oper. Res. 79: 347-360 (2017) - [c38]Kaifeng Yang, Michael Emmerich
Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time. EMO 2017: 685-700 - [c37]Ivo Gonçalves
Unsure when to stop?: ask your semantic neighbors. GECCO 2017: 929-936 - [i4]Ivo Gonçalves, Sara Silva, Carlos M. Fonseca, Mauro Castelli:
Unsure When to Stop? Ask Your Semantic Neighbors. CoRR abs/1706.06195 (2017) - 2016
- [j13]Tobias Kuhn, Carlos M. Fonseca
Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms. Evol. Comput. 24(3): 411-425 (2016) - [j12]Andreia P. Guerreiro
Greedy Hypervolume Subset Selection in Low Dimensions. Evol. Comput. 24(3): 521-544 (2016) - [c36]Carlos M. Fonseca, Elizabeth Fialho Wanner
A quadratic approximation-based local search operator for handling two equality constraints in continuous optimization problems. CEC 2016: 4911-4917 - [c35]Ivo Gonçalves
Arbitrarily Close Alignments in the Error Space: A Geometric Semantic Genetic Programming Approach. GECCO (Companion) 2016: 99-100 - [c34]Claudia R. Correa, Elizabeth F. Wanner
Lyapunov Design of a Simple Step-Size Adaptation Strategy Based on Success. PPSN 2016: 101-110 - [c33]Andreia P. Guerreiro
Hypervolume Sharpe-Ratio Indicator: Formalization and First Theoretical Results. PPSN 2016: 814-823 - [c32]Carola Doerr
Tutorials at PPSN 2016. PPSN 2016: 1012-1022 - [p3]Michael Emmerich
A Multicriteria Generalization of Bayesian Global Optimization. Advances in Stochastic and Deterministic Global Optimization 2016: 229-242 - 2015
- [j11]Daniel Vaz
Representation of the non-dominated set in biobjective discrete optimization. Comput. Oper. Res. 63: 172-186 (2015) - [c31]Rafael Frederico Alexandre, Felipe Campelo
A Comparative Study of Algorithms for Solving the Multiobjective Open-Pit Mining Operational Planning Problems. EMO (2) 2015: 433-447 - [c30]Ivo Gonçalves
Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming. EPIA 2015: 280-285 - [c29]Ivo Gonçalves
On the Generalization Ability of Geometric Semantic Genetic Programming. EuroGP 2015: 41-52 - [c28]Andreia P. Guerreiro
Greedy Hypervolume Subset Selection in the Three-Objective Case. GECCO 2015: 671-678 - [i3]Renaud Lacour, Kathrin Klamroth, Carlos M. Fonseca:
A Box Decomposition Algorithm to Compute the Hypervolume Indicator. CoRR abs/1510.01963 (2015) - 2014
- [j10]Jean Paulo Martins
On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem. Neurocomputing 146: 17-29 (2014) - [c27]Iryna Yevseyeva, Andreia P. Guerreiro, Michael T. M. Emmerich
A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms. PPSN 2014: 672-681 - 2013
- [e3]Robin C. Purshouse
Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Sheffield, UK, March 19-22, 2013. Proceedings. Lecture Notes in Computer Science 7811, Springer 2013, ISBN 978-3-642-37139-4 [contents] - 2012
- [c26]Andreia P. Guerreiro, Carlos M. Fonseca, Michael T. M. Emmerich:
A Fast Dimension-Sweep Algorithm for the Hypervolume Indicator in Four Dimensions. CCCG 2012: 77-82 - 2011
- [c25]Viviane Grunert da Fonseca
The Relationship between the Covered Fraction, Completeness and Hypervolume Indicators. Artificial Evolution 2011: 25-36 - [c24]Carlos M. Fonseca
On the Computation of the Empirical Attainment Function. EMO 2011: 106-120 - [c23]Michael T. M. Emmerich, Carlos M. Fonseca
Computing Hypervolume Contributions in Low Dimensions: Asymptotically Optimal Algorithm and Complexity Results. EMO 2011: 121-135 - 2010
- [j9]Eduardo G. Carrano
Nonlinear Network Optimization - An Embedding Vector Space Approach. IEEE Trans. Evol. Comput. 14(2): 206-226 (2010) - [p2]Viviane Grunert da Fonseca
The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison. Experimental Methods for the Analysis of Optimization Algorithms 2010: 103-130
2000 – 2009
- 2009
- [j8]Nicola Beume, Carlos M. Fonseca
On the Complexity of Computing the Hypervolume Indicator. IEEE Trans. Evol. Comput. 13(5): 1075-1082 (2009) - [c22]José C. Ferreira, Carlos M. Fonseca
Assessing the quality of the relation between scalarizing function parameters and solutions in multiobjective optimization. IEEE Congress on Evolutionary Computation 2009: 1131-1136 - [e2]Matthias Ehrgott, Carlos M. Fonseca
Evolutionary Multi-Criterion Optimization, 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009. Proceedings. Lecture Notes in Computer Science 5467, Springer 2009, ISBN 978-3-642-01019-4 [contents] - [i2]Carlos M. Fonseca, Xavier Gandibleux, Pekka J. Korhonen, Luis Marti, Boris Naujoks, Lothar Thiele, Jyrki Wallenius, Eckart Zitzler:
09041 Working Group on EMO for Interactive Multiobjective Optimization (1st Round). Hybrid and Robust Approaches to Multiobjective Optimization 2009 - 2008
- [j7]Dilip Datta
A multi-objective evolutionary algorithm to exploit the similarities of resource allocation problems. J. Sched. 11(6): 405-419 (2008) - [c21]Dilip Datta, José Rui Figueira, Carlos M. Fonseca, Fernando Tavares-Pereira:
Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study. GECCO 2008: 625-632 - 2007
- [c20]Marisol B. Correia
How redundancy and neutrality may affect evolution on NK fitness landscapes. IEEE Congress on Evolutionary Computation 2007: 2842-2849 - [c19]Carlos M. Fonseca
Decision Making in Evolutionary Optimization (Abstract of Invited Talk). EMO 2007: 3 - [c18]Dilip Datta
Multi-objective Evolutionary Algorithms for Resource Allocation Problems. EMO 2007: 401-416 - [c17]Eduardo G. Carrano, Ricardo H. C. Takahashi
Bi-objective Combined Facility Location and Network Design. EMO 2007: 486-500 - [c16]José C. Ferreira, Carlos M. Fonseca
Methodology to select solutions from the pareto-optimal set: a comparative study. GECCO 2007: 789-796 - [c15]Marisol B. Correia
On the roles of redundancy and neutrality in evolutionary optimization: an experimental study. GECCO 2007: 1504 - [c14]Cristina C. Vieira, Carlos M. Fonseca
A unified model of optimisation problems. GECCO 2007: 1537 - [c13]Carlos M. Fonseca
Preference Articulation in Evolutionary Multiobjective Optimisation. HIS 2007: 4 - [c12]Eduardo G. Carrano, Carlos M. Fonseca
A preliminary comparison of tree encoding schemes for evolutionary algorithms. SMC 2007: 1969-1974 - [p1]Dilip Datta
Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem. Evolutionary Scheduling 2007: 197-236 - 2006
- [c11]Carlos M. Fonseca, Luís Paquete, Manuel López-Ibáñez:
An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator. IEEE Congress on Evolutionary Computation 2006: 1157-1163 - 2005
- [c10]Carlos M. Fonseca, Marisol B. Correia
Developing redundant binary representations for genetic search. Congress on Evolutionary Computation 2005: 1675-1682 - [c9]Carlos M. Fonseca
Exploring the Performance of Stochastic Multiobjective Optimisers with the Second-Order Attainment Function. EMO 2005: 250-264 - [i1]Carlos M. Fonseca, Peter J. Fleming:
Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms. Practical Approaches to Multi-Objective Optimization 2005 - 2004
- [j6]Katya Rodríguez-Vázquez
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming. IEEE Trans. Syst. Man Cybern. Part A 34(4): 531-545 (2004) - 2003
- [j5]António E. B. Ruano
Nonlinear identification of aircraft gas-turbine dynamics. Neurocomputing 55(3-4): 551-579 (2003) - [j4]Eckart Zitzler, Lothar Thiele, Marco Laumanns
Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2): 117-132 (2003) - [c8]Pedro M. Ferreira, António E. B. Ruano, Carlos M. Fonseca:
Genetic assisted selection of RBF model structures for greenhouse inside air temperature prediction. CCA 2003: 576-581 - [e1]Carlos M. Fonseca
Evolutionary Multi-Criterion Optimization, Second International Conference, EMO 2003, Faro, Portugal, April 8-11, 2003, Proceedings. Lecture Notes in Computer Science 2632, Springer 2003, ISBN 3-540-01869-7 [contents] - 2002
- [c7]Eckart Zitzler, Marco Laumanns, Lothar Thiele, Carlos M. Fonseca, Viviane Grunert da Fonseca:
Why Quality Assessment Of Multiobjective Optimizers Is Difficult. GECCO 2002: 666-674 - 2001
- [c6]Viviane Grunert da Fonseca, Carlos M. Fonseca
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function. EMO 2001: 213-225 - [c5]J. M. Lima, A. B. Azevedo, N. M. Duarte, Carlos M. Fonseca, António E. B. Ruano, Peter J. Fleming
Neuro-genetic PID autotuning. ECC 2001: 3899-3904 - 2000
- [c4]N. M. Duarte, António E. B. Ruano, Carlos M. Fonseca, Peter J. Fleming:
Accelerating multi-objective control system design using a neuro-genetic approach. CEC 2000: 392-397
1990 – 1999
- 1999
- [c3]K. J. Shaw, Anne L. Nortcliffe
Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem. CEC 1999: 37-45 - 1998
- [j3]Carlos M. Fonseca
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Trans. Syst. Man Cybern. Part A 28(1): 26-37 (1998) - [j2]Carlos M. Fonseca
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example. IEEE Trans. Syst. Man Cybern. Part A 28(1): 38-47 (1998) - 1996
- [c2]Carlos M. Fonseca
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. PPSN 1996: 584-593 - 1995
- [j1]Carlos M. Fonseca
An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evol. Comput. 3(1): 1-16 (1995) - 1993
- [c1]Carlos M. Fonseca, Peter J. Fleming:
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. ICGA 1993: 416-423
Coauthor Index
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last updated on 2024-09-18 00:16 CEST by the dblp team
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