TORRES project
Traffic prOcessing foR uRban EnvironmentS
Posts by Collection
portfolio
Analysis of ramp events in power systems
Forecasting wind power ramp events using imbalanced time series 
publications
Genetic Algorithms with diversity measures to build classifiers systems
Published in Investigación Operacional, 2015
This paper proposes a modified genetic algorithm that uses diversity measures to select the most diverse and accurate combination of classifiers, demonstrating its effectiveness through applications in two different domains.
Recommended citation: Cabrera Hernández, L., Morales-Hernández, A., Casas Cardoso, G. M., Martínez Jiménez, Y. (2015). "Genetic Algorithms with diversity measures to build classifiers systems." Investigación Operacional. 36(3).
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Building multi-classifiers systems with Ant Colony Optimization
Published in Investigación Operacional, 2017
This paper presents an Ant Colony Optimization-based approach to construct efficient and diverse ensemble of classifiers, optimizing classifier selection to enhance accuracy while maintaining model simplicity.
Recommended citation: Cabrera Hernández, L., González Nápoles, G., Santos, L. R., Morales-Hernández, A., Casas Cardoso, G. M., García Lorenzo, M. M., Martínez Jiménez, Y. (2017). "Building multi-classifiers systems with Ant Colony Optimization." Investigación Operacional. 38(4).
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Diagnosis of the hypertension risk in children applying neurofuzzy systems
Published in Revista Cubana de Informática Médica, 2019
This paper applies neurofuzzy systems to diagnose hypertension risk in children, showing that the NSLV algorithm generates effective diagnostic rules from clinical data to support early detection.
Recommended citation: Morales-Hernández, A., Casas Cardoso, G. M., González Rodríguez, E. F. (2019). "Diagnosis of the hypertension risk in children applying neurofuzzy systems." Revista Cubana de Informática Médica. 11(1).
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Diversity-based selection of learning algorithms: a bagging approach
Published in Investigacion operacional, 2021
This paper proposes a modified bagging algorithm that integrates diverse learning algorithms and optimizations to improve classifier ensembles, demonstrating superior performance over state-of-the-art methods and validating its effectiveness in real biochemical applications.
Recommended citation: Cabrera-Hernández, L., Morales-Hernández, A., Meneses Gómez, M., Meneses Marcel, A., Casas Cardoso, G. M., García Lorenzo, M. M. (2021). "Diversity-based selection of learning algorithms: a bagging approach." Investigación Operacional. 42(4).
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New Diversity Measures Based on the Coverage and Similarity of the Classification
Published in Computación y Sistemas, 2021
This paper proposes two new diversity measures for classifier ensembles, based on coverage and similarity, to improve ensemble accuracy and provides experimental analysis showing their effectiveness and correlation with existing diversity metrics.
Recommended citation: Morales-Hernández, A., Cabrera-Hernández, L., Martínez-Jiménez, Y., García-Lorenzo, M. M., & Casas-Cardoso, G. M. (2021). "New Diversity Measures Based on the Coverage and Similarity of the Classification." Computación y Sistemas. 25(3).
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Measuring wind turbine health using fuzzy-concept-based drifting models
Published in Renewable energy, 2022
This paper introduces two fuzzy concept–based methods for wind turbine health monitoring, enabling interpretable detection of performance degradation by analyzing changes and drifts in power production under varying environmental conditions.
Recommended citation: Jastrzebska, A., Hernández, A. M., Nápoles, G., Salgueiro, Y., & Vanhoof, K. (2022). "Measuring wind turbine health using fuzzy-concept-based drifting models." Renewable Energy. 190.
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Online learning of windmill time series using Long Short-term Cognitive Networks
Published in Expert Systems with Applications, 2022
This paper demonstrates that Long Short-term Cognitive Networks (LSTCNs) provide faster and more accurate online forecasting of windmill time series than traditional RNN-based models, making them well-suited for real-time windmill monitoring and maintenance applications.
Recommended citation: Morales-Hernández, A., Nápoles, G., Jastrzebska, A., Salgueiro, Y., & Vanhoof, K. (2022). "Online learning of windmill time series using Long Short-term Cognitive Networks." Expert Systems with Applications. 205.
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Optimization of Plasma-Assisted Surface Treatment for Adhesive Bonding via Artificial Intelligence.
Published in Proceedings in Engineering Mechanics, 2022
This paper demonstrates that Bayesian optimization with Gaussian process models can effectively support adhesive bonding process optimization, achieving similar joint strength as expert-driven methods with up to 40% less budget and reduced production costs, while ensuring robust and durable bonds.
Recommended citation: Jordens, J., Van Doninck, B., Satrio, N. R., Hernández, A. M., Couckuyt, I., Van Nieuwenhuyse, I., & Witters, M. (2022). "Optimization of plasma-assisted surface treatment for adhesive bonding via artificial intelligence." In 2nd International Conference on Industrial Applications of Adhesives 2022: Selected Contributions of IAA 2022 Proceedings in Engineering Mechanics. (pp. 47-64). Cham: Springer International Publishing.
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Multi-objective Hyperparameter Optimization with Performance Uncertainty.
Published in Communications in Computer and Information Science, 2022
This paper proposes a hybrid multi-objective hyperparameter optimization method that combines Tree-structured Parzen Estimators with Gaussian Process Regression under heterogeneous noise, improving performance under uncertainty compared to stand-alone approaches.
Recommended citation: Morales-Hernández, A., Van Nieuwenhuyse, I., Nápoles, G. (2022). "Multi-objective Hyperparameter Optimization with Performance Uncertainty." In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science. 1684. Springer, Cham.
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A survey on multi-objective hyperparameter optimization algorithms for machine learning
Published in Artificial Intelligence Review, 2022
This article systematically surveys multi-objective hyperparameter optimization methods from 2014–2020, categorizing algorithms, evaluating comparison metrics, and highlighting future research directions.
Recommended citation: Morales-Hernández, A., Van Nieuwenhuyse, I. & Rojas Gonzalez, S. (2022). "A survey on multi-objective hyperparameter optimization algorithms for machine learning." Artificial Intelligence Review. 56.
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Multi-objective optimization of adhesive bonding process in constrained and noisy settings
Published in Communications in Computer and Information Science, 2023
This study leverages Gaussian Process and Logistic Regression models within a Bayesian optimization framework to handle multi-objective, constrained, and uncertain adhesive bonding optimization, achieving efficient solutions with minimal experimental effort.
Recommended citation: Morales-Hernández, A., Van Nieuwenhuyse, I., Rojas Gonzalez, S., Jordens, J., Witters, M., Van Doninck, B. (2023). "Multi-objective Optimization of Adhesive Bonding Process in Constrained and Noisy Settings." In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science. 1824. Springer, Cham.
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Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design applications
Published in Engineering with computers, 2024
This study applies advanced learning and Bayesian optimization to efficiently identify Pareto-optimal adhesive bonding process parameters under multiple noisy objectives and strict experimental constraints, reducing the need for costly physical testing.
Recommended citation: Morales-Hernández, A., Rojas Gonzalez, S., Van Nieuwenhuyse, I. et al. (2024). "Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application." Engineering with Computers. 40.
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Constrained Bayesian Optimization: a review
Published in IEEE Access, 2024
This paper reviews the literature on single-objective constrained Bayesian optimization, classifying methods by metamodels, acquisition functions, and constraint handling, while outlining real-world applications, limitations, and future research directions.
Recommended citation: Amini, S., Vannieuwenhuyse, I., & Morales-Hernandez, A. (2024). "Constrained bayesian optimization: A review." IEEE Access.
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Calibration of vehicular traffic simulation models by local optimization
Published in Transportation, 2025
This paper proposes a novel stochastic, simulation-based calibration method that uses only traffic count data to improve the accuracy, scalability, and real-time applicability of traffic models, demonstrating a 16% accuracy gain over state-of-the-art methods in a Brussels case study.
Recommended citation: Guastella, D.A., Morales-Hernández, A., Cornelis, B., Bontempi, G. (2025). "Calibration of vehicular traffic simulation models by local optimization." Transportation.
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talks
Multi-objective simulation optimization of the adhesive bonding process of materials
Published:
This research applies Bayesian optimization with Gaussian Process Regression and Logistic Regression to identify optimal adhesive bonding process parameters efficiently. The approach reduces experimental effort while guiding designs toward Pareto-optimal solutions for lightweight automotive applications.
Multi-objective hyperparameter optimization with performance uncertainty
Published:
This paper proposes a multi-objective hyperparameter optimization method that combines Tree-structured Parzen Estimators with Gaussian Process Regression to account for uncertainty in performance evaluations. Experiments on analytical test functions and ML problems demonstrate improved optimization results compared to using TPE or GPR alone.
Encore Abstract: Online learning of windmill time series using Long Short-term Cognitive Networks
Published:
This work introduces Long Short-term Cognitive Networks (LSTCNs), a novel gated neural network designed for efficient online learning in windmill farm forecasting. Case study results show that LSTCNs achieve lower forecasting errors and faster training compared to traditional recurrent models.
Tree-structured Parzen Estimators With Uncertainty For Hyperparameter Optimization Of Machine Learning Algorithms
Published:
This work enhances Tree-structured Parzen Estimators (TPE) for hyperparameter optimization by incorporating uncertainty into the evaluation of configurations. The proposed strategy improves algorithm performance compared to standard TPE, which overlooks uncertainty in expected outcomes.
Multi-objective optimization of adhesive bonding process in constrained and noisy settings
Published:
This research applies machine learning models within a Bayesian optimization framework to efficiently identify Pareto-optimal parameters for adhesive bonding processes, balancing strength, cost, and quality constraints. By emulating objective and constraint functions with limited experimental data, the approach reduces the need for costly and time-consuming lab experiments.
Integration of simulation and machine learning to predict the traffic in the event of disruptions
Published:
Decision-making in intelligent transportation systems is complex due to urban traffic’s unpredictable dynamics, particularly when assessing the impact of road works on congestion. To address this, a method combining simulation using SUMO and machine learning with GNN is proposed for traffic prediction in the event of road disruptions.
Leveraging SUMO for Traffic Twins: Experiences in Urban Traffic Processing
Published:
The TORRES project develops an AI-driven framework that integrates diverse mobility data sources with SUMO simulations to improve real-time traffic monitoring, prediction, and decision-making in urban environments. Applied in Brussels and Namur, the framework focuses on three key contributions: traffic calibration, data interpolation, and simulation-assisted prediction for efficient mobility management.
A Tutorial on Smart Process Optimization with Bayesian Optimization: Leveraging AI for Efficient Decision-Making
Published:
In this tutorial, we introduce how Bayesian optimization (BO) can be applied to process-optimization tasks for researchers and practitioners, using an adhesive bonding process as example. We explain the theoretical foundations of BO and provide practical guidance (yes, with code) for implementing it in real-world workflows.
teaching
Data Structure and Algorithms I
Bachelor course - Computer Science & Engineering Informatic, Central University of Las Villas, Faculty of Mathematics, Physics, and Informatics, 2017
Hands-on experience in designing, analyzing, and implementing efficient algorithms and data structures to solve complex computational problems.
Introduction to Java programming
Bachelor course - Biology, Central University of Las Villas, Faculty of Agricultural Sciences, 2017
Introduction to Java programming with a focus on applying object-oriented concepts and computational methods to solve biological problems.
Informatics
Bachelor course - Tourism, Central University of Las Villas, Faculty of Industrial Engineering and Tourism, 2018
Introduction to Informatics fundamentals with practical training in Microsoft Office tools (Word, Excel, PowerPoint, Access), database design, and internet applications for information processing and management.
Data Structure and Algorithms II
Bachelor course - Computer Science & Engineering Informatic, Central University of Las Villas, Faculty of Mathematics, Physics, and Informatics, 2018
Hands-on experience in designing, analyzing, and implementing efficient algorithms and data structures to solve complex computational problems.
Introduction to Information Systems
Master course - Master of Management, Hasselt University, Faculty of Business Informatics, 2020
Study of business information systems with a focus on Business Intelligence, data management, and practical applications in spreadsheets, while exploring digital commerce, security risks, and decision-support tools.