Publications
The following is a list of my publications. You are also invited to check my publication list from other sources:
Google Scholar,
ORCID,
Scopus or
DBLP.
2024
-
A Generalist Reinforcement Learning Agent for Compressing Convolutional Neural Networks.
Gabriel Gonzalez-Sahagun,
Santiago Enrique Conant-Pablos,
Jose Carlos Ortiz-Bayliss,
and Jorge M. Cruz-Duarte,
IEEE Access, 2024
[DOI].
-
Robotic Mobile Fulfillment System: a Systematic Review.
Maria Torcoroma Benavides-Robles,
Gerardo Humberto Valencia-Rivera,
Jorge M. Cruz-Duarte,
Ivan Amaya,
and Jose Carlos Ortiz-Bayliss.
IEEE Access, 2024
[DOI].
-
A systematic review of metaheuristic algorithms in electric power systems optimization.
Gerardo Humberto Valencia-Rivera,
Maria Torcoroma Benavides-Robles,
Alonso Vela Morales,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Juan Gabriel Avina-Cervantes.
Applied Soft Computing, 2024
[DOI].
2023
-
On the Feasibility of using Neural Networks as High-level solvers for the Pod Allocation Problem within Robotic Mobile Fulfillment Systems.
Maria Torcoroma Benavides-Robles,
Jorge M. Cruz-Duarte,
Ivan Amaya,
and Jose Carlos Ortiz-Bayliss.
International Conference on Soft Computing & Machine Intelligence (ISCMI), 2023
[DOI].
-
Using µ Genetic Algorithms for Hyper-heuristic Development: A Preliminary Study on Bin Packing Problems.
Jose Carlos Ortiz-Bayliss,
Julio Juarez,
and Jesus Guillermo Falcon-Cardona.
International Conference on Soft Computing & Machine Intelligence (ISCMI), 2023
[DOI].
-
On the Feasibility of Using a High-level Solver within Robotic Mobile Fulfillment Systems.
Maria Torcoroma Benavides-Robles,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
IEEE Symposium Series on Computational Intelligence (SSCI), 2023
[DOI].
-
SIGNRL: A Population-based Reinforcement Learning method for Continuous Control.
Daniel F. Zambrano-Gutierrez,
Alberto C. Molina-Porras,
Emmanuel Ovalle-Magallanes,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Juan G. Avina-Cervantes
and Jorge M. Cruz-Duarte.
IEEE Symposium Series on Computational Intelligence (SSCI), 2023
[DOI].
-
Forecasting PM2.5 Concentration Levels using Shallow Machine Learning Models on the Monterrey Metropolitan Area in Mexico.
Cesar Alejandro Pozo-Luyo,
Jorge M. Cruz-Duarte,
Ivan Amaya,
and Jose Carlos Ortiz-Bayliss.
Atmospheric Pollution Research, 2023
[DOI].
-
Addressing the Algorithm Selection Problem through an Attention-Based Meta-Learner Approach.
Enrique Diaz de Leon-Hicks,
Santiago Enrique Conant-Pablos,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Applied Sciences, 2023
[DOI].
-
Hyper-heuristics meet Controller Design: Improving Electrical Grid Performance through Microgrids.
Gerardo Humberto Valencia-Rivera,
Jorge M. Cruz-Duarte,
Juan Gabriel Avina-Cervantes,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
IEEE Congress on Evolutionary Computation (CEC), 2023
[DOI|PDF].
-
Recursive Hyper-heuristics for the Job Shop Scheduling Problem.
Alonso Vela,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
IEEE Congress on Evolutionary Computation (CEC), 2023
[DOI|PDF].
-
Automatic Design of Metaheuristics for Practical Engineering Applications.
Daniel F. Zambrano-Gutierrez,
Jorge M. Cruz-Duarte,
Juan Gabriel Avina-Cervantes,
Jose Carlos Ortiz-Bayliss,
Jesus Joaquin Yanes-Borjas,
and Ivan Amaya.
IEEE Access, 2023
[DOI].
2022
-
A Sequence-based Hyper-heuristic for Traveling Thieves.
Daniel Rodríguez,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
Applied Sciences, 2022
[DOI].
-
Identifying Hyper-heuristic Trends through a Text Mining Approach on the Current Literature.
Anna Karen Gárate-Escamilla,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Hugo Terashima Marin,
and Jose Carlos Ortiz-Bayliss.
Applied Sciences, 2022
[DOI].
-
Engagement and social impact in tech-based Citizen Science initiatives for achieving the SDGs: A Systematic Literature Review with a perspective on complex thinking.
Berenice Alfaro-Ponce,
Jorge Sanabria-Z,
Omar Israel González Peña,
Hugo Terashima Marin,
and Jose Carlos Ortiz-Bayliss.
Sustainability, 2022
[DOI].
-
Detection of Violent Behavior using Neural Networks and Pose Estimation.
Kevin B. Kwan-Loo,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
Hugo Terashima-Marin,
and Paul Rad.
IEEE Access, 2022
[DOI].
-
Leveraging a Neuroevolutionary Approach for Classifying Violent Behavior in Video.
Carlos Flores-Munguia,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Computational Intelligence and Neuroscience, 2022
[DOI].
-
A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems.
Jose Manuel Tapia-Avitia,
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Nelishia Pillay.
IEEE Congress on Evolutionary Computation (CEC), 2022
[DOI|PDF].
-
A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics.
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
and Nelishia Pillay.
IEEE Congress on Evolutionary Computation (CEC), 2022
[DOI|PDF].
-
Beyond Hyper-Heuristics: A Squared Hyper-Heuristic model for solving Job Shop Scheduling Problems.
Alonso Vela,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
IEEE Access, 2022
[DOI].
-
MatHH: A Matlab-based Hyper-heuristic Framework.
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
SoftwareX, 2022
[DOI].
2021
-
A Feature-Independent Hyper-heuristic Approach for Solving the Knapsack Problem.
Xavier Sanchez-Diaz,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marín.
Applied Sciences, 2021
[DOI].
-
Hybrid Controller Based on LQR Applied to Interleaved Boost Converter and Microgrids under Power Quality Events.
Gerardo Humberto Valencia-Rivera,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Juan Gabriel Avina-Cervantes.
Energies, 2021
[DOI].
-
Exploring the Knowledge Embedded in Class Visualizations and their Application in Dataset and Extreme Model Compression.
Jose Ricardo Abreu-Pederzini,
Guillermo Arturo Martinez-Mascorro,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima Marin.
Applied Sciences, 2021
[DOI].
-
Naive Hyper-heuristic Online Learning to Generate Unfolded Population-based Metaheuristics to Solve Continuous Optimization Problems.
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
and Nelishia Pillay.
IEEE Symposium Series on Computational Intelligence (SSCI), 2021
[DOI].
-
Sequence-based Selection Hyper-heuristic Model via MAP-Elites.
Melissa Sanchez,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
IEEE Access, 2021
[DOI].
-
Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation.
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant Pablos,
Hugo Terashima-Marin,
and Yong Shi.
Swarm and Evolutionary Computation, 2021
[DOI].
-
Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics.
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
and Nelishia Pillay.
Applied Sciences, 2021
[DOI].
-
Tailoring Job Shop Scheduling Problem Instances through Unified Particle Swarm Optimization.
Alonso Vela,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Ivan Amaya.
IEEE Access, 2021
[DOI].
-
Automated Design of Unfolded Metaheuristics and the Effect of Population Size.
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
and Nelishia Pillay.
IEEE Congress on Evolutionary Computation (CEC), 2021
[DOI|PDF].
-
A Straightforward Framework For Video Retrieval Using CLIP.
Jesus Andres Portillo-Quintero,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Mexican Conference in Pattern Recognition (MCPR), 2021
[DOI|PDF].
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A General Framework based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Andres Eduardo Gutierrez-Rodriguez,
Santiago Enrique Conant Pablos,
and Hugo Terashima-Marin.
Applied Sciences, 2021
[DOI].
-
Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks.
Guillermo Arturo Martinez-Mascorro,
Jose Ricardo Abreu-Pederzini,
Jose Carlos Ortiz-Bayliss,
Angel Garcia-Collantes,
and Hugo Terashima-Marin.
Computation, 2021
[DOI].
-
Algorithm Selection for Solving Educational Timetabling Problems.
Felipe de la Rosa-Rivera,
Jose I. Nunez-Varela,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Expert Systems with Applications, 2021
[DOI].
-
Enhancing Hyper-Heuristics for the Knapsack Problem through Fuzzy Logic.
Frumen Olivas,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
Computational Intelligence and Neuroscience, 2021
[DOI].
-
Solving microelectronic thermal management problems using a generalized spiral optimization algorithm.
Ivan Amaya,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
and Rodrigo Correa.
Applied Intelligence, 2021
[DOI].
2020
-
Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems.
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
Yong Shi,
Hugo Terashima-Marin,
and Nelishia Pillay.
Mathematics, 2020
[DOI].
-
CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search.
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Yong Shi.
SoftwareX, 2020
[DOI].
-
-
Una mirada a las hiper-heurísticas.
Jorge M. Cruz-Duarte,
Ivan Amaya,
and Jose Carlos Ortiz-Bayliss.
Transferencia Tec, 2020
[Web].
-
Exploring Reward-based Hyper-heuristics for the Job-shop Scheduling Problem.
Erick Lara-Cardenas,
Arturo Silva-Galvez,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
Jorge M. Cruz-Duarte,
and Hugo Terashima-Marin.
IEEE Symposium Series on Computational Intelligence (SSCI), 2020
[DOI|PDF].
-
Discovering Action Regions for Solving the Bin Packing Problem through Hyper-heuristics.
Arturo Silva-Galvez,
Jorge Orozco-Sanchez,
Erick Lara-Cardenas,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
Jorge M. Cruz-Duarte,
and Hugo Terashima-Marin.
IEEE Symposium Series on Computational Intelligence (SSCI), 2020
[DOI|PDF].
-
A Genetic Programming Framework for Heuristic Generation for the Job-Shop Scheduling Problem.
Erick Lara-Cardenas,
Xavier Sanchez-Diaz,
Ivan Amaya,
Jorge M. Cruz-Duarte,
and Jose Carlos Ortiz-Bayliss.
Mexican International Conference in Artificial Intelligence (MICAI), 2020
[DOI|PDF].
-
Detecting Suspicious Behavior on Surveillance Videos: Dealing with visual Similarity between Bystanders and Offenders.
Guillermo Arturo Martinez-Mascorro,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
IEEE ANDESCON, 2020
[DOI|PDF].
-
A Systematic Review of Hyper-heuristics on Combinatorial Optimization Problems.
Melissa Sanchez,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
Hector Ceballos,
Hugo Terashima-Marin,
and Ivan Amaya.
IEEE Access, 2020
[DOI].
-
A Preliminary Study on Score-based Hyper-heuristics for Solving the Bin Packing Problem.
Arturo Silva-Galvez,
Erick Lara-Cardenas,
Ivan Amaya,
Jorge M. Cruz-Duarte,
and Jose Carlos Ortiz-Bayliss.
Mexican Conference in Pattern Recognition (MCPR), 2020
[DOI|PDF].
-
A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem.
Xavier Sanchez-Diaz,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
IEEE Congress on Evolutionary Computation (CEC), 2020
[DOI|PDF].
-
Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem.
Fernando Garza-Santisteban,
Ivan Amaya,
Jorge M. Cruz-Duarte,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
Ender Ozcan,
and Hugo Terashima-Marin.
IEEE Congress on Evolutionary Computation (CEC), 2020
[DOI|PDF].
-
A Fuzzy Hyper-Heuristic Approach for the 0-1 Knapsack Problem.
Frumen Olivas,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
IEEE Congress on Evolutionary Computation (CEC), 2020
[DOI|PDF].
-
A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation.
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
IEEE Congress on Evolutionary Computation (CEC), 2020
[DOI|PDF].
2019
-
Influence of Instance Size on Selection Hyper-Heuristics for Job Shop Scheduling Problems.
Fernando Garza-Santisteban,
Jorge M. Cruz-Duarte,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
IEEE Symposium Series on Computational Intelligence (SSCI), 2019
[DOI|PDF].
-
Improving Hyper-heuristic Performance for Job Shop Scheduling Problems using Neural Networks.
Erick Lara-Cardenas,
Xavier Sanchez-Diaz,
Ivan Amaya,
and Jose Carlos Ortiz-Bayliss.
Mexican International Conference in Artificial Intelligence (MICAI), 2019
[DOI|PDF].
-
Hyper-heuristics Reversed: Learning to Combine Solvers by Evolving Instances.
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
IEEE Congress on Evolutionary Computation (CEC), 2019
[DOI|PDF].
-
A Simulated Annealing Hyper-heuristic for Job Shop Scheduling Problems.
Fernando Garza-Santisteban,
Roberto Sanchez-Pamanes,
Luis-Antonio Puente-Rodriguez,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
IEEE Congress on Evolutionary Computation (CEC), 2019
[DOI|PDF].
-
Evolutionary-based tailoring of synthetic instances for the Knapsack problem.
Luis Fernando Plata-Gonzalez,
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
Soft Computing, 2019
[DOI].
-
Selecting Meta-heuristics for Solving Vehicle Routing Problems with Time Windows via Meta-learning.
Andres Eduardo Gutierrez-Rodriguez,
Santiago Enrique Conant-Pablos,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Experts System with Applications, 2019
[DOI].
2018
-
Tailoring Instances of the 1D Bin Packing Problem for Assessing Strengths and Weaknesses of its Solvers.
Ivan Amaya,
Jose Carlos Ortiz-Bayliss,
Santiago Enrique Conant-Pablos,
Hugo Terashima-Marin,
and Carlos Artemio Coello-Coello.
Parallel Problem Solving from Nature (PPSN), 2018
[DOI|PDF].
-
An Experimental Study on Ant Colony Optimization Hyper-heuristics for Solving the Knapsack Problem.
Bronson Duhart,
Fernando Camarena,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Mexican Conference in Pattern Recognition (MCPR), 2018
[DOI|PDF].
-
Enhancing Selection Hyper-heuristics via Feature Transformations.
Ivan Mauricio Amaya-Contreras,
Jose Carlos Ortiz-Bayliss,
Alejandro Rosales-Perez,
Andres Eduardo Gutierrez-Rodriguez,
Santiago Enrique Conant-Pablos,
Hugo Terashima-Marin,
and Carlos Artemio Coello-Coello.
IEEE Computational Intelligence Magazine, 2018
[DOI].
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Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Ivan Mauricio Amaya-Contreras,
Santiago Enrique Conant-Pablos,
and Hugo Terashima-Marin.
Computational Intelligence and Neuroscience, 2018
[DOI].
2017
-
A Quartile-Based Hyper-Heuristic for Solving the 0/1 Knapsack Problem.
Fernando Gomez-Herrera,
Rodolfo A. Ramirez-Valenzuela,
Jose Carlos Ortiz-Bayliss,
Ivan Amaya,
and Hugo Terashima-Marin.
Mexican International Conference in Artificial Intelligence (MICAI), 2017
[DOI|PDF].
-
Improving Hyper-heuristic Performance Through Feature Transformation.
Ivan Mauricio Amaya-Contreras,
Jose Carlos Ortiz-Bayliss,
Andres Eduardo Gutierrez-Rodriguez,
Hugo Terashima-Marin,
and Carlos Artemio Coello-Coello.
IEEE Congress on Evolutionary Computation (CEC), 2017
[DOI|PDF].
-
Evolutionary Multilabel Hyper-Heuristic Design.
Alejandro Rosales-Perez,
Andres Eduardo Gutierrez-Rodriguez,
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Carlos Artemio Coello-Coello.
IEEE Congress on Evolutionary Computation (CEC), 2017
[DOI|PDF].
-
Applying Automatic Heuristic-Filtering to Improve Hyper-heuristic Performance .
Andres Eduardo Gutierrez-Rodriguez,
Jose Carlos Ortiz-Bayliss,
Alejandro Rosales-Perez,
Ivan Mauricio Amaya-Contreras,
Santiago Enrique Conant-Pablos,
Hugo Terashima-Marin,
and Carlos Artemio Coello-Coello.
IEEE Congress on Evolutionary Computation (CEC), 2017
[DOI|PDF].
2016
-
Selection and Generation Hyper-heuristics for Solving the Vehicle Routing Problem with Time Windows.
David Espinoza-Nevarez,
Hugo Terashima-Marin,
Jose Carlos Ortiz-Bayliss,
and Gustavo Gatica.
Genetic and Evolutionary Computation Conference (GECCO), 2016
[DOI|PDF].
-
Grammar-based Selection Hyper-heuristics for Solving Irregular Bin Packing Problems.
Alejandro Sosa-Ascencio,
Hugo Terashima-Marin,
Jose Carlos Ortiz-Bayliss,
and Santiago Enrique Conant-Pablos.
Genetic and Evolutionary Computation Conference (GECCO), 2016
[DOI|PDF].
-
Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Artificial Intelligence Review, 2016
[DOI].
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Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems.
Jorge Humberto Moreno-Scott,
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Computational Intelligence and Neuroscience, 2016
[DOI].
-
A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Cognitive Computation, 2016
[DOI].
2015
-
A Recursive Split, Solve and Join Strategy for Solving Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Dulce Jaqueline Magana-Lozano,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Mexican International Conference in Artificial Intelligence (MICAI), 2015
[DOI|PDF].
-
Lifelong Learning Selection Hyper-heuristics for Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Mexican International Conference in Artificial Intelligence (MICAI), 2015
[DOI|PDF].
2013
-
Learning vector quantization for variable ordering in constraint satisfaction problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Pattern Recognition Letters, 2013
[DOI].
-
A Genetic Programming Hyper-heuristic: Turning Features into Heuristics for Constraint Satisfaction.
Jose Carlos Ortiz-Bayliss,
Ender Ozcan,
Andrew J. Parkes,
and Hugo Terashima-Marin.
UK Workshop on Computational Intelligence (UKCI), 2013
[DOI|PDF].
-
Branching Schemes and Variable Ordering Heuristics for Constraint Satisfaction Problems: Is There Something to Learn?
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), 2013
[DOI|PDF].
-
Automatic Generation of Heuristics for Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Jorge Humberto Moreno-Scott,
and Hugo Terashima-Marin.
International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), 2013
[DOI|PDF].
-
Using Learning Classifier Systems to Design Selective Hyper-Heuristics for Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
IEEE Congress on Evolutionary Computation (CEC), 2013
[DOI|PDF].
-
Exploring Heuristic Interactions in Constraint Satisfaction Problems: A Closer Look at the Hyper-Heuristic Space.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos,
Ender Ozcan and
Andrew J. Parkes.
IEEE Congress on Evolutionary Computation (CEC), 2013
[DOI|PDF].
-
A Supervised Learning Approach to Construct Hyper-heuristics for Constraint Satisfaction.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Mexican Conference on Pattern Recognition (MCPR), 2011
[DOI|PDF].
2012
-
Challenging Heuristics: Evolving Binary Constraint Satisfaction Problems.
Jorge Humberto Moreno-Scott,
Jose Carlos Ortiz-Bayliss,
and Hugo Terashima-Marin.
Genetic and Evolutionary Computation Conference (GECCO), 2012
[DOI].
-
Improving the Performance of Vector Hyper-heuristics through Local Search.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
Santiago Enrique Conant-Pablos,
Ender Ozcan,
and Andrew J. Parkes.
Genetic and Evolutionary Computation Conference (GECCO), 2012
[DOI|PDF].
2011
-
Variable and Value Ordering Decision Matrix Hyper-heuristics: a Local Improvement Approach.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
Ender Ozcan,
Andrew J. Parkes,
and Santiago Enrique Conant-Pablos.
Mexican Conference on Artificial Intelligence (MICAI), 2011
[DOI|PDF].
-
Neural Networks to Guide the Selection of Heuristics within Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
and Santiago Enrique Conant-Pablos.
Mexican Conference on Pattern Recognition (MCPR), 2011
[DOI|PDF].
-
Evolution of Neural Networks Topologies and Learning Parameters to Produce Hyper-heuristics for Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
Peter Ross,
and Santiago Enrique Conant-Pablos.
Genetic and Evolutionary Computation Conference (GECCO), 2011
[DOI|PDF].
-
On the Idea of Evolving Decision Matrix Hyper-Heuristics for Solving Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
Ender Ozcan,
and Andrew J. Parkes.
Genetic and Evolutionary Computation Conference (GECCO), 2011
[DOI|PDF].
2010
-
Mapping the Performance of Heuristics for Constraint Satisfaction.
Jose Carlos Ortiz-Bayliss,
Ender Ozcan,
Andrew J. Parkes,
and Hugo Terashima-Marin.
Congress on Evolutionary Computation (CEC), 2010
[DOI|PDF].
2009
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A Neuro-Evolutionary Approach to Produce General Hyper-heuristics for the Dynamic Variable Ordering in Hard Binary Constraint Satisfaction Problems.
Jose Carlos Ortiz-Bayliss,
Hugo Terashima-Marin,
Peter Ross,
Jorge Ivan Fuentes-Rosado,
and Manuel Valenzuela-Rendon.
Genetic and Evolutionary Computation Conference (GECCO), 2009
[DOI|PDF].
2008
-
Using Hyper-heuristics for the Dynamic Variable Ordering in Hard Binary Constraint Satisfaction Problems.
Hugo Terashima-Marin,
Jose Carlos Ortiz-Bayliss,
Peter Ross,
and Manuel Valenzuela-Rendon.
Mexican International Conference on Artificial Intelligence (MICAI), 2008
[DOI|PDF].
-
Hyper-heuristics for the Dynamic Variable Ordering in Constraint Satisfaction Problems.
Hugo Terashima-Marin,
Jose Carlos Ortiz-Bayliss,
Peter Ross,
and Manuel Valenzuela-Rendon.
Genetic and Evolutionary Computation Conference (GECCO), 2008
[DOI|PDF].
