Gregorio Toscano Headshot

Department

  • Electrical Engineering and Computer Science

    • School

  • School of Engineering

    • Expertise

  • Multi-objective Optimization
  • Computational Intelligence
  • Machine Learning
  • Genetic Algorithms
  • Artificial Intelligence

    • Bio

    Gregorio Toscano, Ph.D., is an assistant professor in the Department of Electrical Engineering and Computer Science, joining in fall 2024. He holds an M.Sc. from the University of Veracruz (Mexico) and a Ph.D. from the Center for Research and Advanced Studies (Mexico), where he later worked. Before his current role, he was with Michigan State University. Dr. Toscano is known for developing state-of-the-art multi-objective optimization algorithms, such as the first micro-genetic and particle swarm optimization, which have been applied to numerous real-world problems. His work in computational intelligence has garnered over 8,000 citations. Outside of work, he enjoys biking, playing board games, and grilling food.

    Publications

    1. Gregorio Toscano, Hoda Razavi, A. Pouyan Nejadhashemi, Kalyanmoy Deb, and Lewis Linker, “Large-scale Multi- objective Optimization for Watershed Planning and Assessment,” IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024, no. 1 (2024): 1–12, issn: 2168-2216, https://doi.org/10.1109/TSMC.2024.3361679
    2. Gregorio Toscano, Hoda Razavi, A. Pouyan Nejadhashemi, Kalyanmoy Deb, and Lewis Linker, “Utilizing Innovization to Solve Large-Scale Multi-Objective Chesapeake Bay Watershed Problem,” in 2023 IEEE Congress on Evolutionary Computation (CEC) (2023), 1–8, https://doi.org/10.1109/CEC53210.2023.10254161
    3. Gregorio Toscano, Juan Hernández-Suárez, Julian Blank, Pouyan Nejadhashemi, and Kalyanmoy Deb, “Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed,” in 2022 IEEE Congress on Evolutionary Computation (CEC’2022), ed. Alessandro Sperduti and Marco Gori General Co-Chairs of IEEE WCCI 2022, (Best paper award) (Padua, Italy: IEEE Press, July 2022), 1–9, https://doi.org/10.1109/CEC55065.2022.9870286
    4. Samuel Omar Tovias-Alanis, Wilfrido Gómez-Flores, and Gregorio Toscano-Pulido, “Evolutionary Instance Selection Based on Preservation of the Data Probability Density Function,” Computación y Sistemas 26, no. 2 (April 2022): 853– 866, issn: 2007-9737, https://doi.org/10.13053/CyS-26-2-4255
    5. Samuel Omar Tovias-Alanis, Wilfrido Gómez-Flores, Gregorio Toscano-Pulido, and Juan Humberto Sossa-Azuela, “Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification,” in Pattern Recognition. MCPR 2022, ed. Osslan Osiris Vergara-Villegas, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, and José Arturo Olvera-López, (Best paper award) (Springer. LNCS Vol. 13264, 2022), 105–1115, isbn: 978-3-031-07750-0, https://doi.org/10.1007/978-3-031-07750-0_10
    6. Juan Hernández-Suárez, Gregorio Toscano, Pouyan Nejadhashemi, and Kalyanmoy Deb, “Development of an Effi- cient Optimization Framework for Improving Water Quality in the Chesapeake Bay Watershed,” in American Geo- physical Union Fall Meeting 2021 (AGU-2021) (New Orleans, Louisiana, December 2021)
    7. Samuel Tovias-Alanis, Wilfrido Gomez-Flores, and Gregorio Toscano-Pulido, “Instance Selection Based on Linkage Trees,” in 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), CCE 2021 (Mexico City, Mexico: IEEE, 2021), 1–6, https://doi.org/10.1109/CCE53527.2021.9633116
    8. Miguel Santiago-Duran, J.L. Gonzalez-Compean, André Brinkmann, Hugo G. Reyes-Anastacio, Jesus Carretero, Raf- faele Montella, and Gregorio Toscano-Pulido, “A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers,” Future Generation Computer Systems 106 (May 2020): 304– 319, issn: 0167-739X, https://doi.org/10.1016/j.future.2020.01.014
    9. Auraham Camacho, Gregorio Toscano, Ricardo Landa, and Hisao and Ishibuchi, “Indicator-Based Weight Adaptation for Solving Many-Objective Optimization Problems,” in 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 (East Lansing, U.S.A: Springer-Verlag New York, Inc., 2019), 216–228, https://doi.org/10. 1007/978-3-030-12598-1_18
    10. Ricardo Landa, Giomara Larraga, and Gregorio Toscano, “Use of a Goal-constraint-based Approach for Finding the Region of Interest in Multi-objective Problems,” Journal of Heuristics 25, no. 1 (February 2019): 107–139, issn: 1381- 1231, https://doi.org/10.1007/s10732-018-9387-8
    11. Kalyanmoy Deb, Rayan Hussein, Proteek Roy, and Gregorio Toscano, “A Taxonomy for Metamodeling Methods for Evolutionary Multi-Objective Optimization,” IEEE Transactions on Evolutionary Computation 23, no. 1 (February 2019): 104–116, issn: 1089-778X, https://doi.org/10.1109/TEVC.2018.2828091
    12. Javier Rubio-Loyola, Christian Aguilar-Fuster, Gregorio Toscano-Pulido, Rashid Mijumbi, and Joan Serrat, “Enhancing Metaheuristic-based Online Embedding in Network Virtualization Environments,” IEEE Transactions on Network and Service Management 15, no. 1 (March 2018): 200–2016, issn: 1932-4537, https://doi.org/10.1109/TNSM.2017.2742666
    13. Kalyanmoy Deb, Rayan Hussein, Proteek Roy, and Gregorio Toscano, “Classifying Metamodeling Methods for Evo- lutionary Multi-objective Optimization: First Results,” in 9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 10173, EMO 2017 (Münster, Germany: Springer-Verlag New York, Inc., 2017), 160–175, isbn: 978-3-319-54156-3, https://doi.org/10.1007/978-3-319-54157-0_12
    14. Alan Diaz-Manriquez, Gregorio Toscano, and Carlos A. Coello Coello, “Comparison of Metamodeling Techniques in Evolutionary Algorithms,” Soft Computing 21, no. 19 (October 2017): 5647–5663, issn: 1433-7479, https://doi.org/10. 1007/s00500-016-2140-z
    15. Gregorio Toscano, Ricardo Landa, Giomara Larraga, and Guillermo Leguizamon, “On the use of stochastic ranking for parent selection in differential evolution for constrained optimization,” Soft Computing 21, no. 16 (August 2017): 4617–4633, issn: 1432-7643, https://doi.org/10.1007/s00500-016-2073-6
    16. Gregorio Toscano and Kalyanmoy Deb, “Study of the Approximation of the Fitness Landscape and the Ranking Process of Scalarizing Functions for Many-objective Problems,” in 2016 IEEE Congress on Evolutionary Computation (CEC’2016), ed. Kay Chen Tan and General Co-Chair of IEEE WCCI 2016 (Vancouver, Canada: IEEE Press, July 2016), 4358–4365, https://doi.org/10.1109/CEC.2016.7744344
    17. Alan Diaz-Manriquez, Gregorio Toscano, Jose Hugo Barron-Zambrano, and Edgar Tello-Leal, “R2-Based Multi/Many- Objective Particle Swarm Optimization,” article ID 1898527, Computational Intelligence and Neuroscience 2016 (May 2016), issn: 1687-5265, https://doi.org/10.1155/2016/1898527
    18. Alan Diaz-Manriquez, Gregorio Toscano, Jose Hugo Barron-Zambrano, and Edgar Tello-Leal, “A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms,” article ID 9420460, Computational Intelligence and Neuroscience 2016 (May 2016), issn: 1687-5265, https://doi.org/10.1155/2016/9420460
    19. Oliver Schutze, Christian Dominguez-Medina, Nareli Cruz-Cortes, Luis Gerardo de la Fraga, Jian-Qiao Sun, Gregorio Toscano, and Ricardo Landa, “A Scalar Optimization Approach for Averaged Hausdorff Approximations of the Pareto Front,” Engineering Optimization 48, no. 9 (2016): 1593–1617, issn: 0305-215X, https://doi.org/10.1080/0305215X.
    20. Mario Garza-Fabre, Gregorio Toscano-Pulido, and Eduardo Rodriguez-Tello, “Multi-objectivization, Fitness Land- scape Transformation and Search Performance: A Case of Study on the HP model for Protein Structure Prediction,” European Journal of Operational Research 243, no. 2 ( June 2015): 405–422, issn: 0377-2217, https://doi.org/10.1016/j. ejor.2014.06.009

    Read Full List of Publications →