Dr. ERIK VALDEMAR CUEVAS JIMÉNEZ
erik.cuevas@academicos.udg.mx
Departamento de adscripción:
INGENIERIA ELECTRO-FOTONICA
Nombramiento: PROFESOR E INVESTIGADOR TITULAR "C"
Categoría: DEFINITIVO
Resumen curricular:
El doctor Erik Cuevas es profesor en el Centro Universitario de Ciencias Exactas e ingenierías de la Universidad de Guadalajara. En 2006 obtuvo doctor en Inteligencia Artificial por la Universidad Libre de Berlín en Alemania. Es miembro del Sistema Nacional de Investigadores Nivel III, con un índice H de 33. Ha publicado varios artículos y libros. Actualmente es editor de las revistas Expert System with Applications, Soft Computing, Mathematics and Computers in Simulation, Artificial Intelligence Review, Applied Soft Computing, Applied Mathematical Modelling, International Journal of Machine Learning and Cybernetics, Pattern Analysis and Applications, Evolutionary Intelligence, International Journal of Computational Intelligence Systems. Sur áreas de interés son los algoritmos metaheurísticos, el machine learning y procesamiento de imagen.
Perfil de Investigador SNII:
Nivel SNII:
III
Área del conocimiento:
VIII. Ingenierías y Desarrollo Tecnológico, y
Campo de investigación:
INTELIGENCIA ARTIFICIAL
Periodo vigente:
1 de enero de 2021
-
31 de diciembre de 2026
Perfil PRODEP:
Inicio de la vigencia:
30 de agosto de 2022
Fin de la vigencia:
29 de agosto de 2028
Bases de datos bibliográficas:
Publicaciones del académico:
2026 - 1 articulos.
2025 - 49 articulos.
- Overcoming Center-Bias behavior: A Metaheuristic Algorithm with Dual Operators for Optimized Search and Refinement
- JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition
- DANet a lightweight dilated attention network for malaria parasite detection
- Corrigendum to “Image segmentation with Cellular Automata” [Heliyon Volume 10, Issue 10, May 2024, Article e31152] (Heliyon (2024) 10(10), (S2405844024071834), (10.1016/j.heliyon.2024.e31152))
- Corrigendum to “Image segmentation with Cellular Automata” [Heliyon Volume 10, Issue 10, May 2024, Article e31152] (Heliyon (2024) 10(10), (S2405844024071834), (10.1016/j.heliyon.2024.e31152))
- Bezier-based exploration and hexagonal crossover exploitation: a novel metaheuristic approach
- Simulating flood situations in urban hydrology using a diffusion model in complex networks
- Filling space swarm optimization (FSSO): a metaheuristic algorithm with divided agent strategies and diamond crossover
- Expansion-Trajectory Optimization (ETO): A Dual-Operator Metaheuristic for Balanced Global and Local Search
- Cellular neighbors optimizer: a novel metaheuristic approach inspired by the cellular automata and agent-based modeling for global optimization
- Advanced Metaheuristics: Novel Approaches for Complex Problem Solving
- A novel metaheuristic algorithm using structured population and virtual particles
- Impact of Programming Languages on Learning Performance
- Optimization Strategies: A Decade of Metaheuristic Algorithm Development
- Optimization Strategies: A Decade of Metaheuristic Algorithm Development
- An improved swarm optimization algorithm using exploration and evolutionary game theory for efficient exploitation
- Analyzing metaheuristic algorithms performance and the causes of the zero-bias problem: a different perspective in benchmarks
- A novel cheetah optimizer hybrid approach based on opposition-based learning (OBL) and diversity metrics
- JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition
- Corrigendum to “Image segmentation with Cellular Automata” (Heliyon (2024) 10(10), (S2405844024071834), (10.1016/j.heliyon.2024.e31152))
- Corrigendum to “Image segmentation with Cellular Automata” (Heliyon (2024) 10(10), (S2405844024071834), (10.1016/j.heliyon.2024.e31152))
- Corrigendum to “Image segmentation with Cellular Automata”[Heliyon Volume 10, Issue 10, May 2024, Article e31152]
- Metaheuristic Techniques for Fine‐Tuning Parameter of Complex Systems
- Advanced Metaheuristics: Novel Approaches for Complex Problem Solving
- Corrigendum to “Image segmentation with Cellular Automata”[Heliyon Volume 10, Issue 10, May 2024, Article e31152]
- Cellular neighbors optimizer: a novel metaheuristic approach inspired by the cellular automata and agent-based modeling for global optimization
- Learning Strategies in Metaheuristics
- Population Initialization Based on the Gibbs Sampling Methodology
- Diversity-Opposition Hybridization of the Cheetah Optimizer for Global Optimization
- Integrating agent-based models and clustering methods for improving image segmentation
- Metaheuristic Techniques for Fine‐Tuning Parameter of Complex Systems
- Techniques of Machine Learning Mixed with Metaheuristic Methods
- Metaheuristic Methods for Clustering
- Metaheuristic Methods for Regression
- Metaheuristic Methods for Classification
- Main Metaheuristic Techniques
- Fundamentals of Machine Learning
- Fundamental Machine Learning Methods
- DC Motors: Modeling, Designing and Building with 3D Printers
- DANet a lightweight dilated attention network for malaria parasite detection
- A Hybrid Metaheuristic Approach Integrating Population Structure and Collective Characteristics
- Differential Evolution Exploration Enhancement through Latin Hypercube Sampling Trajectories: A Novel Hybrid Scheme
- New knowledge-based approach to population initialization using Moving Least Squares (MLS) and Radial Basis Functions (RBF) methods
- Agent-Based Modeling Approaches as Metaheuristic Methods
- Corrigendum to “Image segmentation with Cellular Automata”[Heliyon Volume 10, Issue 10, May 2024, Article e31152]
- Analyzing metaheuristic algorithms performance and the causes of the zero-bias problem: a different perspective in benchmarks
- Modeling Optimization Techniques Inspired by Yellow Saddle Goatfish Behavior
- Locust Search Algorithm: A Novel Swarm Intelligence Approach for Complex Optimization
- An Algorithm for Global Optimization Inspired by Collective Animal Behavior