Summary

Angel Casas Ordaz received a B.S. degree in Electronic Engineering from the Instituto Tecnológico de Ciudad Juárez in 2018 and later the M.Sc. degree in Electronics and Computer Engineering from the Universidad de Guadalajara at the Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI) in 2022; He is currently a Ph.D. student in Electronics and Computer Science at the same campus. During his postgraduate research, he specialized in the field of Evolutionary Computation and Image Segmentation. His current research interests include Evolutionary Computation and Artificial Intelligence, specializing in Metaheuristic Algorithms in the Automatic Control and Intelligent Systems Research Group.

Edited Journals

ICCK Contributions

Articles 3 Edited 0
Free Access | Research Article | 20 December 2025
A Comparison of Evolutionary Computation Techniques for Parameter Estimation of Chaotic Systems
by
Jorge Gálvez
,
Gustavo R. Hernández
,
Omar Avalos
,
Angel Casas-Ordaz
,
Marco Perez-Cinseros
,
Diego Oliva
ICCK Transactions on Swarm and Evolutionary Learning | Volume 1, Issue 2: 83-93, 2025 | DOI: 10.62762/TSEL.2025.913117
Abstract
In recent years, Parameter Estimation (PE) has become a topic of growing interest due to its broad applications in science and engineering. An important application is the identification of Chaotic Systems (CS), which enables synchronization and control of chaotic behavior. However, the parameter estimation of CS is a highly nonlinear and multidimensional optimization problem where traditional approaches are often unsuitable. To overcome these limitations, Evolutionary Computation Techniques (ECT) have been widely adopted to tackle complex nonlinear optimization tasks. Recently, classical and modern ECT methods have been proposed for estimating the parameters of chaotic systems. However, mos... More >
Graphical Abstract
A Comparison of Evolutionary Computation Techniques for Parameter Estimation of Chaotic Systems
Free Access | Research Article | 20 November 2025
A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding
by
Omar Alvarez
,
Luis A. Beltran
,
Angel Casas-Ordaz
,
Jorge Ramos-Frutos
,
Mario A. Navarro-Velázquez
,
Oscar Ramos-Soto
,
Diego Oliva
ICCK Transactions on Swarm and Evolutionary Learning | Volume 1, Issue 2: 50-82, 2025 | DOI: 10.62762/TSEL.2025.417356
Abstract
Metaheuristic Algorithms (MAs) are commonly used in the scope of digital image processing, in particular, image segmentation processes. This is evident in Multilevel Thresholding (MTH) methods, where the optimal threshold configuration must be found to produce high-quality segmented images. Minimum Cross-Entropy (MCE) is one of the most prominent techniques for MTH due to its simplicity and efficiency. This article proposes a comparison of recent MAs that have not yet been implemented for image segmentation. Six recently published MAs were implemented and tested on nine complicated images selected from the BSDS300 dataset. Analyzing the results reveals the best algorithm when MCE is used as... More >
Graphical Abstract
A Comparative Analysis of Recent Metaheuristic Algorithms for Image Segmentation Using the Minimum Cross-Entropy for Multilevel Thresholding
Free Access | Research Article | 31 May 2025
Enhanced Differential Evolution: Multi-Strategy Approach with Neighborhood-Based Selection
by
Elivier Reyes-Davila
,
Eduardo H. Haro
,
Angel Casas-Ordaz
,
Diego Oliva
,
Saúl Zapotecas-Martínez
,
Mohammed El-Abd
ICCK Transactions on Swarm and Evolutionary Learning | Volume 1, Issue 1: 12-24, 2025 | DOI: 10.62762/TSEL.2025.182681
Abstract
The Differential Evolution (DE) has stood as a cornerstone of Evolutionary Computation (EC), inspiring numerous approaches. Despite its foundational role, the selection stage of DE has received little attention, with only 2% of documented modifications in the literature on this aspect. Recent research has underscored the potential for significant algorithmic improvement through thoughtful modifications to this critical stage, particularly in accelerating the exploitation phase. This study introduces a novel EC strategy rooted in DE principles. To enhance algorithmic exploration, a systematic decision-making process regarding function evaluations is employed to select between two of the most... More >
Graphical Abstract
Enhanced Differential Evolution: Multi-Strategy Approach with Neighborhood-Based Selection