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Carlos M. Fonseca

Carlos M. Fonseca

D-Index & Metrics

Computer Science

D-Index
43
Citations
23480
World Ranking
7737
National Ranking
16

Overview

Carlos M. Fonseca is affiliated with the University of Coimbra in Portugal and has a research portfolio spanning computer science and engineering. Their work primarily focuses on advanced optimization algorithms and related fields within computational theory and applied mathematics.

The scientist's research topics include:

  • Advanced Multi-Objective Optimization Algorithms
  • Probabilistic and Robust Engineering Design
  • Optimal Experimental Design Methods
  • Metaheuristic Optimization Algorithms Research
  • Vehicle Emissions and Performance
  • Traffic Control and Management
  • Transportation Planning and Optimization

The core fields of study for Carlos M. Fonseca cover:

  • Computer Science
  • Engineering

Their work extends into several subfields, including:

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Artificial Intelligence
  • Management Science and Operations Research
  • Control and Systems Engineering

They have contributed to multiple publication venues, notably:

  • arXiv (Cornell University)
  • Mathematical Methods of Operations Research
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • ACM Computing Surveys
  • Sustainable Cities and Society

A selection of recent papers includes:

  • The Hypervolume Indicator, 2021, ACM Computing Surveys
  • The Hypervolume Indicator: Problems and Algorithms, 2020, arXiv (Cornell University)
  • MobiWise: Eco-routing decision support leveraging the Internet of Things, 2022, Sustainable Cities and Society
  • On the rectangular knapsack problem: approximation of a specific quadratic knapsack problem, 2020, Mathematical Methods of Operations Research
  • A Compressive Receding Horizon Approach for Smart Home Energy Management, 2021, IEEE Access

Frequent co-authors collaborating with Carlos M. Fonseca include:

  • Andreia P. Guerreiro
  • Luís Paquete
  • Stefan Ruzika
  • Michael Stiglmayr
  • Kathrin Klamroth

In addition to journal articles, they have published a book titled Evolutionary Multi-Criterion Optimization in 2023 through Springer Science+Business Media.

Best Publications

  • Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

    Carlos M. Fonseca;Peter J. Fleming

  • Performance assessment of multiobjective optimizers: an analysis and review

    E. Zitzler;L. Thiele;M. Laumanns;C.M. Fonseca

  • An overview of evolutionary algorithms in multiobjective optimization

    Carlos M. Fonseca;Peter J. Fleming

  • Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

    C.M. Fonseca;P.J. Fleming

  • An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator

    C.M. Fonseca;L. Paquete;M. Lopez-Ibanez

  • On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers

    Carlos M. Fonseca;Peter J. Fleming

  • Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example

    C.M. Fonseca;P.J. Fleming

  • Multiobjective genetic algorithms made easy: selection sharing and mating restriction

    C.M. Fonseca;P.J. Fleming

  • On the Complexity of Computing the Hypervolume Indicator

    N. Beume;C.M. Fonseca;M. Lopez-Ibanez;L. Paquete

  • Multiobjective genetic algorithms with application to control engineering problems.

    Carlos Manuel Mira da Fonseca

  • Multiobjective genetic algorithms

    C. M. Fonseca;P. J. Fleming

  • Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function

    Viviane Grunert da Fonseca;Carlos M. Fonseca;Andreia O. Hall

  • The Hypervolume Indicator: Computational Problems and Algorithms

    Andreia P. Guerreiro;Carlos M. Fonseca;Luís Paquete

  • Genetic Algorithms in Control Systems Engineering

    P.J. Fleming;C.M. Fonseca

  • Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming

    K. Rodriguez-Vazquez;C.M. Fonseca;P.J. Fleming

  • Why quality assessment of multiobjective optimizers is difficult

    Eckart Zitzler;Marco Laumanns;Lothar Thiele;Carlos M. Fonseca

  • Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms

    Carlos M. Fonseca;Peter J. Fleming

  • Evolutionary Multi-Criterion Optimization

    Matthias Ehrgott;Carlos M. Fonseca;Xavier Gandibleux;Jin-Kao Hao

  • Methodology to select solutions from the pareto-optimal set: a comparative study

    J. C. Ferreira;C. M. Fonseca;A. Gaspar-Cunha

  • Exploring the performance of stochastic multiobjective optimisers with the second-order attainment function

    Carlos M. Fonseca;Viviane Grunert da Fonseca;Luís Paquete

Frequent Co-Authors

Peter J. Fleming
Peter J. Fleming University of Sheffield
Sara Silva
Sara Silva University of Lisbon
Kathrin Klamroth
Kathrin Klamroth University of Wuppertal
Eckart Zitzler
Eckart Zitzler Lucerne University of Applied Sciences and Arts
Lothar Thiele
Lothar Thiele ETH Zurich
Michael Emmerich
Michael Emmerich Leiden University
Manuel López-Ibáñez
Manuel López-Ibáñez University of Manchester
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
Marco Laumanns
Marco Laumanns ZF Friedrichshafen (Germany)
José Rui Figueira
José Rui Figueira Instituto Superior Técnico

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