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Gregorio Toscano Pulido
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2020 – today
- 2024
- [j16]Kalyanmoy Deb, A. Pouyan Nejadhashemi
, Gregorio Toscano, Hoda Razavi, Lewis C. Linker:
Leveraging innovization and transfer learning to optimize best management practices in large-scale watershed management. Environ. Model. Softw. 180: 106161 (2024) - [j15]Gregorio Toscano Pulido
Large-Scale Multiobjective Optimization for Watershed Planning and Assessment. IEEE Trans. Syst. Man Cybern. Syst. 54(6): 3471-3483 (2024) - 2023
- [c40]Gregorio Toscano, Hoda Razavi, A. Pouyan Nejadhashemi, Kalyanmoy Deb, Lewis C. Linker:
Utilizing Innovization to Solve Large-Scale Multi-Objective Chesapeake Bay Watershed Problem. CEC 2023: 1-8 - 2022
- [j14]Samuel Omar Tovias-Alanis, Wilfrido Gómez-Flores, Gregorio Toscano Pulido:
Evolutionary Instance Selection Based on Preservation of the Data Probability Density Function. Computación y Sistemas 26(2) (2022) - [c39]Gregorio Toscano, Juan Sebastian Hernandez-Suarez
Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed. CEC 2022: 1-9 - [c38]Samuel Omar Tovias-Alanis
Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification. MCPR 2022: 105-115 - 2021
- [c37]Samuel Omar Tovias-Alanis, Wilfrido Gómez-Flores, Gregorio Toscano Pulido:
Instance Selection Based on Linkage Trees. CCE 2021: 1-6 - 2020
- [j13]Miguel Santiago-Duran, José Luis González Compeán
A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. Future Gener. Comput. Syst. 106: 304-319 (2020)
2010 – 2019
- 2019
- [j12]Ricardo Landa Becerra
Use of a goal-constraint-based approach for finding the region of interest in multi-objective problems. J. Heuristics 25(1): 107-139 (2019) - [j11]Kalyanmoy Deb
A Taxonomy for Metamodeling Frameworks for Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Comput. 23(1): 104-116 (2019) - [c36]Auraham Camacho, Gregorio Toscano Pulido, Ricardo Landa Becerra, Hisao Ishibuchi:
Indicator-Based Weight Adaptation for Solving Many-Objective Optimization Problems. EMO 2019: 216-228 - 2018
- [j10]Javier Rubio-Loyola, Christian Aguilar-Fuster, Gregorio Toscano Pulido
Enhancing Metaheuristic-Based Online Embedding in Network Virtualization Environments. IEEE Trans. Netw. Serv. Manag. 15(1): 200-216 (2018) - 2017
- [j9]Gregorio Toscano Pulido
On the use of stochastic ranking for parent selection in differential evolution for constrained optimization. Soft Comput. 21(16): 4617-4633 (2017) - [j8]Alan Díaz-Manríquez
Comparison of metamodeling techniques in evolutionary algorithms. Soft Comput. 21(19): 5647-5663 (2017) - [c35]Kalyanmoy Deb, Rayan Hussein, Proteek Chandan Roy, Gregorio Toscano:
Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results. EMO 2017: 160-175 - 2016
- [j7]Alan Díaz-Manríquez
R2-Based Multi/Many-Objective Particle Swarm Optimization. Comput. Intell. Neurosci. 2016: 1898527:1-1898527:10 (2016) - [j6]Alan Díaz-Manríquez
A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms. Comput. Intell. Neurosci. 2016: 9420460:1-9420460:14 (2016) - [c34]Gregorio Toscano, Kalyanmoy Deb:
Study of the approximation of the fitness landscape and the ranking process of scalarizing functions for many-objective problems. CEC 2016: 4358-4365 - 2015
- [j5]Mario Garza-Fabre
Constraint-handling through multi-objective optimization: The hydrophobic-polar model for protein structure prediction. Comput. Oper. Res. 53: 128-153 (2015) - [j4]Mario Garza-Fabre
Multi-objectivization, fitness landscape transformation and search performance: A case of study on the hp model for protein structure prediction. Eur. J. Oper. Res. 243(2): 405-422 (2015) - [j3]Javier Rubio-Loyola, Gregorio Toscano Pulido
Business-driven policy optimization for service management. Int. J. Netw. Manag. 25(2): 113-140 (2015) - 2013
- [j2]Mario Garza-Fabre
Comparative Analysis of Different Evaluation Functions for Protein Structure Prediction Under the HP Model. J. Comput. Sci. Technol. 28(5): 868-889 (2013) - [c33]Alan Díaz-Manríquez
A hybrid local search operator for multiobjective optimization. IEEE Congress on Evolutionary Computation 2013: 173-180 - [c32]Ricardo Landa Becerra, Carlos Artemio Coello Coello
Goal-constraint: Incorporating preferences through an evolutionary ε-constraint based method. IEEE Congress on Evolutionary Computation 2013: 741-747 - [c31]Alan Díaz-Manríquez
A ranking method based on the R2 indicator for many-objective optimization. IEEE Congress on Evolutionary Computation 2013: 1523-1530 - [c30]Alan Díaz-Manríquez
On the use of a BSP Tree to create local surrogate models. IEEE Congress on Evolutionary Computation 2013: 2540-2547 - [c29]Mario Garza-Fabre
Handling constraints in the HP model for protein structure prediction by multiobjective optimization. IEEE Congress on Evolutionary Computation 2013: 2728-2735 - 2012
- [c28]Mario Garza-Fabre
Multiobjectivizing the HP Model for Protein Structure Prediction. EvoCOP 2012: 182-193 - [c27]Mario Garza-Fabre
Locality-based multiobjectivization for the HP model of protein structure prediction. GECCO 2012: 473-480 - [c26]Ricardo Landa Becerra
An approach for estimating separability and its application on high dimensional optimization. GECCO 2012: 951-958 - [c25]Mario Garza-Fabre
An Improved Multiobjectivization Strategy for HP Model-Based Protein Structure Prediction. PPSN (2) 2012: 82-92 - 2011
- [c24]Alan Díaz-Manríquez
A Surrogate-Based Intelligent Variation Operator for Multiobjective Optimization. Artificial Evolution 2011: 13-24 - [c23]Jorge Sebastian Hernández-Domínguez, Gregorio Toscano Pulido
A Multi-objective Particle Swarm Optimizer Enhanced with a Differential Evolution Scheme. Artificial Evolution 2011: 169-180 - [c22]Jorge Sebastian Hernández-Domínguez, Gregorio Toscano Pulido
A comparison on the search of particle swarm optimization and differential evolution on multi-objective optimization. IEEE Congress on Evolutionary Computation 2011: 1978-1985 - [c21]Mario Garza-Fabre
Effective ranking + speciation = Many-objective optimization. IEEE Congress on Evolutionary Computation 2011: 2115-2122 - [c20]Alan Díaz-Manríquez
On the selection of surrogate models in evolutionary optimization algorithms. IEEE Congress on Evolutionary Computation 2011: 2155-2162 - [c19]Mario Garza-Fabre
Comparing alternative energy functions for the HP model of protein structure prediction. IEEE Congress on Evolutionary Computation 2011: 2307-2314 - [c18]Alan Díaz-Manríquez, Gregorio Toscano Pulido, Ricardo Landa Becerra:
A Long-term Memory Approach for Dynamic Multiobjective Evolutionary Algorithms. IJCCI (ECTA-FCTA) 2011: 333-337 - [c17]M. Nava-Ortiz, Wilfrido Gómez-Flores
Evaluation of Binarization Algorithms for Camera-Based Devices. MCPR 2011: 164-173 - 2010
- [c16]Mario Garza-Fabre
Two novel approaches for many-objective optimization. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c15]Alan Díaz-Manríquez, Gregorio Toscano Pulido, José Gabriel Ramírez-Torres:
Handling Dynamic Multiobjective Problems with Particle Swarm Optimization. ICAART (1) 2010: 337-342
2000 – 2009
- 2009
- [c14]Mario Garza-Fabre
Alternative Fitness Assignment Methods for Many-Objective Optimization Problems. Artificial Evolution 2009: 146-157 - [c13]Angelina Jane Reyes Medina, Gregorio Toscano Pulido, José Gabriel Ramírez-Torres:
A Comparative Study of Neighborhood Topologies for Particle Swarm Optimizers. IJCCI 2009: 152-159 - [c12]Mario Garza-Fabre
Ranking Methods for Many-Objective Optimization. MICAI 2009: 633-645 - 2008
- [c11]Ezra Federico Parra-González, Gabriel Ramírez-Torres, Gregorio Toscano Pulido
Motion Planning for Cooperative Multi-robot Box-Pushing Problem. IBERAMIA 2008: 382-391 - [c10]Juan Carlos Elizondo-Leal
Multi-robot Exploration and Mapping Using Self Biddings. IBERAMIA 2008: 392-401 - [c9]Juan Carlos Elizondo-Leal, Gabriel Ramírez-Torres, Gregorio Toscano Pulido:
Multi-robot Exploration and Mapping Using Self Biddings and Stop Signals. MICAI 2008: 615-625 - 2007
- [c8]Gregorio Toscano Pulido, Carlos A. Coello Coello, Luis V. Santana-Quintero:
EMOPSO: A Multi-Objective Particle Swarm Optimizer with Emphasis on Efficiency. EMO 2007: 272-285 - [c7]Israel Vite Silva, Nareli Cruz Cortés, Gregorio Toscano Pulido, Luis Gerardo de la Fraga:
Optimal Triangulation in 3D Computer Vision Using a Multi-objective Evolutionary Algorithm. EvoWorkshops 2007: 330-339 - [c6]Oliver Schütze
A Memetic PSO Algorithm for Scalar Optimization Problems. SIS 2007: 128-134 - 2005
- [c5]Mario Villalobos-Arias
A proposal to use stripes to maintain diversity in a multi-objective particle swarm optimizer. SIS 2005: 22-29 - 2004
- [j1]Carlos A. Coello Coello
Handling Multiple Objectives With Particle Swarm Optimization. IEEE Trans. Evol. Comput. 8(3): 256-279 (2004) - [c4]Gregorio Toscano Pulido
A constraint-handling mechanism for particle swarm optimization. IEEE Congress on Evolutionary Computation 2004: 1396-1403 - [c3]Gregorio Toscano Pulido, Carlos A. Coello Coello:
Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer. GECCO (1) 2004: 225-237 - 2003
- [c2]Gregorio Toscano Pulido, Carlos A. Coello Coello:
The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. EMO 2003: 252-266 - 2001
- [c1]Carlos A. Coello Coello, Gregorio Toscano Pulido:
A Micro-Genetic Algorithm for Multiobjective Optimization. EMO 2001: 126-140
Coauthor Index
aka: Carlos Artemio Coello Coello
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last updated on 2024-11-13 23:47 CET by the dblp team
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