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Santiago E. Conant-Pablos
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2020 – today
- 2024
- [j16]Gabriel Gonzalez-Sahagun
, Santiago Enrique Conant-Pablos
A Generalist Reinforcement Learning Agent for Compressing Convolutional Neural Networks. IEEE Access 12: 51100-51114 (2024) - [j15]Alonso Javier Amado-Garfias
Improving Armed People Detection on Video Surveillance Through Heuristics and Machine Learning Models. IEEE Access 12: 111818-111831 (2024) - [j14]Gabriel Gonzalez-Sahagun
A Generalist Reinforcement Learning Agent for Compressing Multiple Convolutional Networks Using Singular Value Decomposition. IEEE Access 12: 136131-136147 (2024) - [c32]Diego Acosta-Ugalde, Jorge M. Cruz-Duarte, Santiago E. Conant-Pablos, Jesús Guillermo Falcón-Cardona:
Beyond 'Novel' Metaphor-based Metaheuristics: An Interactive Algorithm Design Software. CEC 2024: 1-8 - [c31]Gustavo de Jesús Machado-Guillén, Jorge M. Cruz-Duarte, Santiago E. Conant-Pablos, Katarzyna Filus:
Harnessing Machine Learning for Reliable Weather Forecasting: Meteorological Impact on Sustainable Energy in Monterrey. CEC 2024: 1-8 - 2023
- [c30]Diego Acosta-Ugalde, Santiago Enrique Conant-Pablos
Data Mining and Analysis of NLP Methods in Students Evaluation of Teaching. MICAI (2) 2023: 28-38 - 2022
- [j13]Kevin B. Kwan-Loo, José Carlos Ortiz-Bayliss
Detection of Violent Behavior Using Neural Networks and Pose Estimation. IEEE Access 10: 86339-86352 (2022) - [i2]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
- [j12]Frumen Olivas
Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic. Comput. Intell. Neurosci. 2021: 8834324:1-8834324:17 (2021) - [j11]Jorge M. Cruz-Duarte
Hyper-Heuristics to customise metaheuristics for continuous optimisation. Swarm Evol. Comput. 66: 100935 (2021) - 2020
- [j10]Fernando Isunza Fonseca, Diego Villarreal Gutiérrez, Santiago Enrique Conant-Pablos:
A New Methodology for Solving the Vehicle Routing Problem with Time Windows Using Weighted Heuristics. Res. Comput. Sci. 149(10): 21-36 (2020) - [c29]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 - [c28]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 - [c27]Xavier Sánchez
A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem. CEC 2020: 1-8
2010 – 2019
- 2019
- [j9]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) - [j8]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) - [c26]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 - [c25]Iván Amaya, José Carlos Ortiz-Bayliss, Santiago E. Conant-Pablos
Hyper-heuristics Reversed: Learning to Combine Solvers by Evolving Instances. CEC 2019: 1790-1797 - [c24]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
- [j7]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) - [j6]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) - [c23]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
- [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 - 2016
- [j5]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) - [j4]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) - [j3]José Carlos Ortiz-Bayliss
A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems. Cogn. Comput. 8(3): 429-441 (2016) - [j2]Alejandro Sosa-Ascencio, Gabriela Ochoa
Grammar-based generation of variable-selection heuristics for constraint satisfaction problems. Genet. Program. Evolvable Mach. 17(2): 119-144 (2016) - [c21]Alejandro Sosa-Ascencio, Hugo Terashima-Marín
Grammar-based Selection Hyper-heuristics for Solving Irregular Bin Packing Problems. GECCO (Companion) 2016: 111-112 - 2015
- [c20]José Carlos Ortiz-Bayliss
A Recursive Split, Solve, and Join Strategy for Solving Constraint Satisfaction Problems. MICAI (Special Sessions) 2015: 73-79 - [c19]José Carlos Ortiz-Bayliss
Lifelong Learning Selection Hyper-heuristics for Constraint Satisfaction Problems. MICAI (1) 2015: 190-201 - 2014
- [c18]Edgar Reyna-Ayala, Santiago E. Conant-Pablos
Assembling Similar Tracking Approaches in Order to Strengthen Performance. MCPR 2014: 201-210 - 2013
- [j1]José Carlos Ortiz-Bayliss
Learning vector quantization for variable ordering in constraint satisfaction problems. Pattern Recognit. Lett. 34(4): 423-432 (2013) - [c17]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 - [c16]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 - [c15]José Carlos Ortiz-Bayliss
A Supervised Learning Approach to Construct Hyper-heuristics for Constraint Satisfaction. MCPR 2013: 284-293 - [c14]José Carlos Ortiz-Bayliss
Branching Schemes and Variable Ordering Heuristics for Constraint Satisfaction Problems: Is There Something to Learn? NICSO 2013: 329-342 - 2012
- [c13]Jorge Humberto Moreno-Scott, José Carlos Ortiz-Bayliss
Challenging heuristics: evolving binary constraint satisfaction problems. GECCO 2012: 409-416 - [c12]José Carlos Ortiz-Bayliss
Improving the performance of vector hyper-heuristics through local search. GECCO 2012: 1269-1276 - [c11]Eunice López-Camacho
The impact of the bin packing problem structure in hyper-heuristic performance. GECCO (Companion) 2012: 1545-1546 - [c10]Alan I. Torres-Nogales, Santiago E. Conant-Pablos
Local Features Classification for Adaptive Tracking. MICAI (1) 2012: 146-157 - [c9]Dulce J. Magaña-Lozano, Santiago E. Conant-Pablos
Exploring the Solution of Course Timetabling Problems through Heuristic Segmentation. MICAI (1) 2012: 347-358 - 2011
- [c8]Santiago Enrique Conant-Pablos
Evolving solutions of mixed-model assembly line balancing problems by chaining heuristic optimization methods: track: evolutionary combinatorial optimization and meta-heuristics. GECCO (Companion) 2011: 49-50 - [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 - [p1]Santiago E. Conant-Pablos, Rolando R. Hernández-Cisneros, Hugo Terashima-Marín:
Feature Selection for the Classification of Microcalcifications in Digital Mammograms using Genetic Algorithms, Sequential Search and Class Separability. Genetic and Evolutionary Computation: Medical Applications 2011
2000 – 2009
- 2009
- [c4]Santiago E. Conant-Pablos
Pipelining Memetic Algorithms, Constraint Satisfaction, and Local Search for Course Timetabling. MICAI 2009: 408-419 - 2007
- [c3]Rolando R. Hernández-Cisneros, Hugo Terashima-Marín
Comparison of Class Separability, Forward Sequential Search and Genetic Algorithms for Feature Selection in the Classification of Individual and Clustered Microcalcifications in Digital Mammograms. ICIAR 2007: 911-922 - 2004
- [c2]Santiago E. Conant-Pablos
A Method to Obtain Sensing Requirements in Robotic Assemblies by Geometric Reasoning. MICAI 2004: 862-871 - 2003
- [c1]Santiago E. Conant-Pablos, Horacio Martinez-Alfaro, Katsushi Ikeuchi:
Sensing requirements for robotic assembly from an analysis of critical contact transitions. SMC 2003: 1834-1839
Coauthor Index
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