• Learning the progression patterns of treatments using a probabilistic generative model 

  Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)

  Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model ...

• Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach 

  Boutaib, S.; Elarbi, M.; Bechikh, S.; Coello, C.A.; Said, L. B. (2022-11-01)

  Context: Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of ...

• On the Construction of Pareto-Compliant Combined Indicators 

  Falcón-Cardona, J.G.; Emmerich, M.; Coello, C.A. (2022-08-12)

  The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ...

• A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization 

  Li, L.; Li, Y.; Lin, Q.; Ming, Z.; Coello, C.A. (2022-10-01)

  Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection ...

• An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization 

  Lin, Q.; Wu, X.; Ma, L.; Li, J.; Gong, M.; Coello, C.A. (2022-08-01)

  Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, ...

• Multiple source transfer learning for dynamic multiobjective optimization 

  Ye, Y.; Lin, Q.; Ma, L.; Wong, K. C.; Gong, M.; Coello, C.A. (2022-08-01)

  Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs ...

• A self-organizing weighted optimization based framework for large-scale multi-objective optimization 

  Li, Y.; Li, L.; Lin, Q.; Wong, K. C.; Ming, Z.; Coello, C.A. (2022-07-01)

  The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization ...

• Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters 

  Fernández, E.; Rangel-Valdez, N.; Cruz-Reyes, L.; Gomez-Santillan, C.; Coello, C.A. (2022-07-01)

  Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP ...

• Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations 

  Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2022-06-01)

  The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently available. However, some of its components ...

• A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering 

  Yu, Q.; Lin, Q.; Zhu, Z.; Wong, K. C.; Coello, C.A. (2022-06-01)

  In this paper, a dynamic multi-objective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEA-PRAC. As the Pareto-optimal solutions and fronts of dynamic multi-objective ...

• AdaSwarm: Augmenting Gradient-Based Optimizers in Deep Learning with Swarm Intelligence 

  Mohapatra, R.; Saha, S.; Coello, C.A.; Bhattacharya, A.; Dhavala, S. S.; Saha, S. (2022-04-01)

  This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially ...

• Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking 

  Rivera, G.; Coello, C.A.; Cruz-Reyes, L.; Fernandez, E.R.; Gomez-Santillan, C.; Rangel-Valdez, N. (2022-03-01)

  In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the ...

• Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering 

  del Barrio, E.; Inouzhe, H.; Loubes, J.-M. (2022-10-20)

  We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the ...

• Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks 

  García, F.; Lee, D.-J.; Mendoza-Garcés, F. J.; Irigoyen-Miró, S.; Legarreta-Olabarrieta, M. J.; García-Gutiérrez, S.; Arostegui, I. (2023-04)

  Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology. Objective:To ...

• Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques 

  Hayet-Otero, M.; García, F.; Lee, D.-J.; Martínez-Minaya, J.; España Yandiola, P.P.; Urrutia Landa, I.; Nieves Ermecheo, M.; Quintana, J.M.; Menéndez, R.; Torres, A.; Zalacain Jorge, R.; Arostegui, I. (2023)

  With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse ...

• Variable selection in high-dimensional data: application in a SARS-CoV-2 pneumonia clinical data-set 

  Hayet-Otero, M. (2021-09-15)

  As a result of the COVID-19 pandemic that collapsed hospitals in some countries, numerous studies have been carried out to understand the development of the disease and how it affects patients with different characteristics, ...

• Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques 

  Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-10-09)

  In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs ...

• Development of machine learning system for airway prediction from facial image with mobile device 

  Mendoza Garcés, F.; García, F.; García, S.; García, B.; Vargas, J.; Lee, D.-J. (2022-06)

  Goals: A reliable prognostic tool for a difficult airway (DA) may enhance patients’ safety during orotracheal intubation by decreasing unanticipated DAs. We aim to examine the applicability of an Artificial Intelligence-Deep ...

• Desarrollo de sistema de Machine Learning para la prediccion de vía aérea a partir de imagen facial con dispositivo movil 

  Mendoza Garcés, F.; Irigoyen Miró, S.; García, F.; González, J.; García, S.; Telletxea, S. (2022-04)

  El manejo de una vía aérea difícil (VAD) representa aún una causa importante de lesiones relacionadas con la anestesia, cuyas complicaciones son potencialmente mortales. El notable interés en la predicción de VAD ha provocado ...

• Impacto cuantitativo de la contaminación en la probabilidad de muerte por neumonía por SARS-CoV-2 

  Bronte Moreno, O.; Urrutia Landa, I.; Martínez-Minaya, J.; García, F.; Lee, D.-J.; Jódar Samper, A.; García Hontoria, P.; Arostegui, I.; Artaraz Ereño, A.; Menéndez Villanueva, R.; Méndez Ocaña, R.; Torres Marti, A.; Cillóniz, C.; Zalacain Jorge, R.; Ruiz Iturriaga, L.A.; Serrano Fernández, L.; España Yandiola, P.P. (2021-11)

  Introducción La evidencia científica disponible señala que la contaminación del aire exterior podría agravar la severidad de la COVID-19 y por ende, incrementar las probabilidades de fallecimiento. Material y métodos Estudio ...

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