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José Carlos Ortiz-Bayliss
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2020 – today
- 2024
- [j28]Maria Torcoroma Benavides-Robles
, Gerardo Humberto Valencia-Rivera, Jorge M. Cruz-Duarte
Robotic Mobile Fulfillment System: A Systematic Review. IEEE Access 12: 16767-16782 (2024) - [j27]Gabriel Gonzalez-Sahagun
A Generalist Reinforcement Learning Agent for Compressing Convolutional Neural Networks. IEEE Access 12: 51100-51114 (2024) - [j26]Alonso Javier Amado-Garfias
Improving Armed People Detection on Video Surveillance Through Heuristics and Machine Learning Models. IEEE Access 12: 111818-111831 (2024) - [j25]Gabriel Gonzalez-Sahagun
A Generalist Reinforcement Learning Agent for Compressing Multiple Convolutional Networks Using Singular Value Decomposition. IEEE Access 12: 136131-136147 (2024) - [j24]Alonso Javier Amado-Garfias
Automatic Selection of Machine Learning Models for Armed People Identification. IEEE Access 12: 175952-175968 (2024) - [j23]Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles
A systematic review of metaheuristic algorithms in electric power systems optimization. Appl. Soft Comput. 150: 111047 (2024) - [j22]Jose M. Tapia-Avitia, Jorge M. Cruz-Duarte
Analysing hyper-heuristics based on Neural Networks for the automatic design of population-based metaheuristics in continuous optimisation problems. Swarm Evol. Comput. 89: 101616 (2024) - [c52]Guillermo Pérez-Espinosa, Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín, Nelishia Pillay:
Tailoring Metaheuristics for Designing Thermodynamic-Optimal Cooling Devices for Microelectronic Thermal Management Applications. CEC 2024: 1-8 - [c51]Daniel F. Zambrano-Gutierrez, Jorge M. Cruz-Duarte, José Carlos Ortiz-Bayliss, Iván Amaya, Juan Gabriel Aviña-Cervantes:
Beyond Traditional Tuning: Unveiling Metaheuristic Operator Trends in PID Control Tuning for Automatic Voltage Regulation. CEC 2024: 1-8 - [c50]José Carlos Ortiz-Bayliss
Missing Data and Their Effect on Algorithm Selection for the Bin Packing Problem. MCPR 2024: 34-43 - [c49]José Eduardo Zárate-Aranda
An Exploratory Study on Machine-Learning-Based Hyper-heuristics for the Knapsack Problem. MCPR 2024: 119-128 - [c48]José Eduardo Zárate-Aranda
Exploring Classificational Cellular Automaton Hyper-heuristics for Solving the Knapsack Problem. MICAI (2) 2024: 57-69 - 2023
- [j21]Daniel F. Zambrano-Gutierrez
Automatic Design of Metaheuristics for Practical Engineering Applications. IEEE Access 11: 7262-7276 (2023) - [c47]Gerardo Humberto Valencia-Rivera, José Carlos Ortiz-Bayliss, Jorge M. Cruz-Duarte, Iván Amaya, Juan Gabriel Aviña-Cervantes:
Hyper-Heuristics Meet Controller Design: Improving Electrical Grid Performance through Microgrids. CEC 2023: 1-8 - [c46]Alonso Vela, Jorge M. Cruz-Duarte, José Carlos Ortiz-Bayliss, Iván Amaya:
Recursive Hyper-Heuristics for the Job Shop Scheduling Problem. CEC 2023: 1-8 - [c45]Maria Torcoroma Benavides-Robles
On the Feasibility of Using a High-Level Solver within Robotic Mobile Fulfillment Systems. SSCI 2023: 1274-1279 - [c44]Daniel F. Zambrano-Gutierrez, Alberto C. Molina-Porras, Emmanuel Ovalle-Magallanes, Iván Amaya, José Carlos Ortiz-Bayliss, Juan Gabriel Aviña-Cervantes, Jorge M. Cruz-Duarte:
SIGNRL: A Population-Based Reinforcement Learning Method for Continuous Control. SSCI 2023: 1443-1448 - 2022
- [j20]Alonso Vela, Jorge M. Cruz-Duarte
Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling Problems. IEEE Access 10: 43981-44007 (2022) - [j19]Kevin B. Kwan-Loo, José Carlos Ortiz-Bayliss
Detection of Violent Behavior Using Neural Networks and Pose Estimation. IEEE Access 10: 86339-86352 (2022) - [j18]Jorge M. Cruz-Duarte
MatHH: A Matlab-based Hyper-Heuristic framework. SoftwareX 18: 101047 (2022) - [c43]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Nelishia Pillay:
A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics. CEC 2022: 1-8 - [c42]Jose M. Tapia-Avitia
A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems. CEC 2022: 1-8 - [i5]Roberto García-Torres, Alitzel Adriana Macias-Infante, Santiago Enrique Conant-Pablos
Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem. CoRR abs/2211.13922 (2022) - 2021
- [j17]Alonso Vela, Jorge M. Cruz-Duarte
Tailoring Job Shop Scheduling Problem Instances Through Unified Particle Swarm Optimization. IEEE Access 9: 66891-66914 (2021) - [j16]Melissa Sánchez
Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites. IEEE Access 9: 116500-116527 (2021) - [j15]Jorge M. Cruz-Duarte
Solving microelectronic thermal management problems using a generalized spiral optimization algorithm. Appl. Intell. 51(8): 5622-5643 (2021) - [j14]Frumen Olivas
Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic. Comput. Intell. Neurosci. 2021: 8834324:1-8834324:17 (2021) - [j13]Guillermo A. Martínez-Mascorro
Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks. Comput. 9(2): 24 (2021) - [j12]Felipe de la Rosa-Rivera, José I. Nuñez-Varela
Algorithm selection for solving educational timetabling problems. Expert Syst. Appl. 174: 114694 (2021) - [j11]Jorge M. Cruz-Duarte
Hyper-Heuristics to customise metaheuristics for continuous optimisation. Swarm Evol. Comput. 66: 100935 (2021) - [c41]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Nelishia Pillay:
Automated Design of Unfolded Metaheuristics and the Effect of Population Size. CEC 2021: 1155-1162 - [c40]Jesús Andrés Portillo-Quintero
A Straightforward Framework for Video Retrieval Using CLIP. MCPR 2021: 3-12 - [c39]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Nelishia Pillay:
Naïve Hyper-heuristic Online Learning to Generate Unfolded Population-based Metaheuristics to Solve Continuous Optimization Problems. SSCI 2021: 1-8 - [i4]Jesús Andrés Portillo-Quintero, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín:
A Straightforward Framework For Video Retrieval Using CLIP. CoRR abs/2102.12443 (2021) - 2020
- [j10]Melissa Sánchez
A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems. IEEE Access 8: 128068-128095 (2020) - [j9]Jorge M. Cruz-Duarte
CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search. SoftwareX 12: 100628 (2020) - [c38]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Santiago Enrique Conant-Pablos
A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous optimisation. CEC 2020: 1-8 - [c37]Fernando Garza-Santisteban, Iván Amaya, Jorge Cruz, José Carlos Ortiz-Bayliss, Ender Özcan
Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem. CEC 2020: 1-8 - [c36]Frumen Olivas, Iván Amaya, José Carlos Ortiz-Bayliss, Santiago E. Conant-Pablos
A Fuzzy Hyper-Heuristic Approach for the 0-1 Knapsack Problem. CEC 2020: 1-8 - [c35]Xavier Sánchez
A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem. CEC 2020: 1-8 - [c34]Arturo Silva-Gálvez
A Preliminary Study on Score-Based Hyper-heuristics for Solving the Bin Packing Problem. MCPR 2020: 318-327 - [c33]Erick Lara-Cárdenas
A Genetic Programming Framework for Heuristic Generation for the Job-Shop Scheduling Problem. MICAI (1) 2020: 284-295 - [c32]Arturo Silva-Gálvez, Jorge Orozco-Sanchez, Erick Lara-Cárdenas, José Carlos Ortiz-Bayliss, Iván Amaya, Jorge M. Cruz-Duarte, Hugo Terashima-Marín
Discovering Action Regions for Solving the Bin Packing Problem through Hyper-heuristics. SSCI 2020: 822-828 - [c31]Erick Lara-Cárdenas, Arturo Silva-Gálvez, José Carlos Ortiz-Bayliss, Iván Amaya, Jorge M. Cruz-Duarte, Hugo Terashima-Marín
Exploring Reward-based Hyper-heuristics for the Job-shop Scheduling Problem. SSCI 2020: 3133-3140 - [i3]Guillermo A. Martínez-Mascorro, José R. Abreu-Pederzini, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín:
Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks. CoRR abs/2005.02142 (2020) - [i2]Guillermo A. Martínez-Mascorro, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín:
Detecting Suspicious Behavior: How to Deal with Visual Similarity through Neural Networks. CoRR abs/2007.15235 (2020)
2010 – 2019
- 2019
- [j8]Andrés Eduardo Gutiérrez-Rodríguez, Santiago E. Conant-Pablos
Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning. Expert Syst. Appl. 118: 470-481 (2019) - [j7]Luis Fernando Plata-González, Iván Amaya, José Carlos Ortiz-Bayliss
Evolutionary-based tailoring of synthetic instances for the Knapsack problem. Soft Comput. 23(23): 12711-12728 (2019) - [c30]Fernando Garza-Santisteban, Roberto Sánchez-Pámanes, Luis Antonio Puente Rodríguez, Iván Amaya, José Carlos Ortiz-Bayliss, Santiago E. Conant-Pablos
A Simulated Annealing Hyper-heuristic for Job Shop Scheduling Problems. CEC 2019: 57-64 - [c29]Iván Amaya, José Carlos Ortiz-Bayliss, Santiago E. Conant-Pablos
Hyper-heuristics Reversed: Learning to Combine Solvers by Evolving Instances. CEC 2019: 1790-1797 - [c28]Erick Lara-Cárdenas
Improving Hyper-heuristic Performance for Job Shop Scheduling Problems Using Neural Networks. MICAI 2019: 150-161 - [c27]Fernando Garza-Santisteban, Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Santiago Enrique Conant-Pablos
Influence of Instance Size on Selection Hyper-Heuristics for Job Shop Scheduling Problems. SSCI 2019: 1708-1715 - 2018
- [j6]Iván Amaya, José Carlos Ortiz-Bayliss, Alejandro Rosales-Pérez
Enhancing Selection Hyper-Heuristics via Feature Transformations. IEEE Comput. Intell. Mag. 13(2): 30-41 (2018) - [j5]José Carlos Ortiz-Bayliss
Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems. Comput. Intell. Neurosci. 2018: 6103726:1-6103726:14 (2018) - [c26]Bronson Duhart, Fernando Camarena
An Experimental Study on Ant Colony Optimization Hyper-Heuristics for Solving the Knapsack Problem. MCPR 2018: 62-71 - [c25]Iván Amaya, José Carlos Ortiz-Bayliss
Tailoring Instances of the 1D Bin Packing Problem for Assessing Strengths and Weaknesses of Its Solvers. PPSN (2) 2018: 373-384 - [i1]Iván Amaya, José Carlos Ortiz-Bayliss, Alejandro Rosales-Pérez, Andrés Eduardo Gutiérrez-Rodríguez, Santiago E. Conant-Pablos, Hugo Terashima-Marín, Carlos A. Coello Coello:
Enhancing Selection Hyper-heuristics via Feature Transformations. CoRR abs/1812.05070 (2018) - 2017
- [c24]Iván Amaya, José Carlos Ortiz-Bayliss, Andrés Eduardo Gutiérrez-Rodríguez, Hugo Terashima-Marín
Improving hyper-heuristic performance through feature transformation. CEC 2017: 2614-2621 - [c23]Alejandro Rosales-Pérez
Evolutionary multilabel hyper-heuristic design. CEC 2017: 2622-2629 - [c22]Andrés Eduardo Gutiérrez-Rodríguez, José Carlos Ortiz-Bayliss, Alejandro Rosales-Pérez
Applying automatic heuristic-filtering to improve hyper-heuristic performance. CEC 2017: 2638-2644 - [c21]Fernando Gómez-Herrera, Rodolfo A. Ramirez-Valenzuela, José Carlos Ortiz-Bayliss, Iván Amaya, Hugo Terashima-Marín
A Quartile-Based Hyper-heuristic for Solving the 0/1 Knapsack Problem. MICAI (1) 2017: 118-128 - 2016
- [j4]José Carlos Ortiz-Bayliss
Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems. Artif. Intell. Rev. 46(3): 327-349 (2016) - [j3]Jorge Humberto Moreno-Scott, José Carlos Ortiz-Bayliss
Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems. Comput. Intell. Neurosci. 2016: 7349070:1-7349070:15 (2016) - [j2]José Carlos Ortiz-Bayliss
A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems. Cogn. Comput. 8(3): 429-441 (2016) - [c20]Alejandro Sosa-Ascencio, Hugo Terashima-Marín
Grammar-based Selection Hyper-heuristics for Solving Irregular Bin Packing Problems. GECCO (Companion) 2016: 111-112 - [c19]David Espinoza-Nevárez, José Carlos Ortiz-Bayliss
Selection and Generation Hyper-heuristics for Solving the Vehicle Routing Problem with Time Windows. GECCO (Companion) 2016: 139-140 - 2015
- [c18]José Carlos Ortiz-Bayliss
A Recursive Split, Solve, and Join Strategy for Solving Constraint Satisfaction Problems. MICAI (Special Sessions) 2015: 73-79 - [c17]José Carlos Ortiz-Bayliss
Lifelong Learning Selection Hyper-heuristics for Constraint Satisfaction Problems. MICAI (1) 2015: 190-201 - 2013
- [j1]José Carlos Ortiz-Bayliss
Learning vector quantization for variable ordering in constraint satisfaction problems. Pattern Recognit. Lett. 34(4): 423-432 (2013) - [c16]José Carlos Ortiz-Bayliss
Using learning classifier systems to design selective hyper-heuristics for constraint satisfaction problems. IEEE Congress on Evolutionary Computation 2013: 2618-2625 - [c15]José Carlos Ortiz-Bayliss
Exploring heuristic interactions in constraint satisfaction problems: A closer look at the hyper-heuristic space. IEEE Congress on Evolutionary Computation 2013: 3307-3314 - [c14]José Carlos Ortiz-Bayliss
A Supervised Learning Approach to Construct Hyper-heuristics for Constraint Satisfaction. MCPR 2013: 284-293 - [c13]José Carlos Ortiz-Bayliss
Automatic Generation of Heuristics for Constraint Satisfaction Problems. NICSO 2013: 315-327 - [c12]José Carlos Ortiz-Bayliss
Branching Schemes and Variable Ordering Heuristics for Constraint Satisfaction Problems: Is There Something to Learn? NICSO 2013: 329-342 - [c11]José Carlos Ortiz-Bayliss
A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction. UKCI 2013: 183-190 - 2012
- [c10]Jorge Humberto Moreno-Scott, José Carlos Ortiz-Bayliss
Challenging heuristics: evolving binary constraint satisfaction problems. GECCO 2012: 409-416 - [c9]José Carlos Ortiz-Bayliss
Improving the performance of vector hyper-heuristics through local search. GECCO 2012: 1269-1276 - 2011
- [c8]José Carlos Ortiz-Bayliss
On the idea of evolving decision matrix hyper-heuristics for solving constraint satisfaction problems. GECCO (Companion) 2011: 255-256 - [c7]José Carlos Ortiz-Bayliss
Evolution of neural networks topologies and learning parameters to produce hyper-heuristics for constraint satisfaction problems. GECCO (Companion) 2011: 261-262 - [c6]José Carlos Ortiz-Bayliss
Neural Networks to Guide the Selection of Heuristics within Constraint Satisfaction Problems. MCPR 2011: 250-259 - [c5]José Carlos Ortiz-Bayliss
Variable and Value Ordering Decision Matrix Hyper-heuristics: A Local Improvement Approach. MICAI (1) 2011: 125-136 - 2010
- [c4]José Carlos Ortiz-Bayliss
Mapping the performance of heuristics for Constraint Satisfaction. IEEE Congress on Evolutionary Computation 2010: 1-8
2000 – 2009
- 2009
- [c3]José Carlos Ortiz-Bayliss
A neuro-evolutionary approach to produce general hyper-heuristics for the dynamic variable ordering in hard binary constraint satisfaction problems. GECCO 2009: 1811-1812 - 2008
- [c2]Hugo Terashima-Marín
Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems. GECCO 2008: 571-578 - [c1]Hugo Terashima-Marín, José Carlos Ortiz-Bayliss, Peter Ross, Manuel Valenzuela-Rendón:
Using Hyper-heuristics for the Dynamic Variable Ordering in Binary Constraint Satisfaction Problems. MICAI 2008: 407-417
Coauthor Index
Iván Amaya
aka: Ivan M. Amaya-Contreras
aka: Ivan M. Amaya-Contreras
Santiago E. Conant-Pablos
aka: Santiago Enrique Conant-Pablos
aka: Santiago Enrique Conant-Pablos
[j26] [j24] [j22] [c52] [c50] [j19] [c42] [i5] [j14] [j13] [j12] [j11] [c40] [i4] [j10] [j9] [c38] [c37] [c36] [c35] [c32] [c31] [i3] [i2] [j8] [j7] [c30] [c29] [c27] [j6] [j5] [c26] [c25] [i1] [c24] [c23] [c22] [c21] [j4] [j3] [j2] [c20] [c19] [c18] [c17] [j1] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1]
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