Alejandro Morales Hernández

Alejandro Morales Hernández

He/Him

Postdoctoral researcher

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

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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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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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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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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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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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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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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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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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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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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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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Conference Papers

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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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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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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