Efrén Mezura-Montes

Universidad Veracruzana | UV · Departamento de Inteligencia Artificial

The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which requires a computational cost for evaluating each objective function. Therefore, surrogate models were implemented to minimize this disadvantage. Neverthele...

Drought prediction is a complex phenomenon that impacts human activities and the environment. For this reason, predicting its behavior is crucial to mitigating such effects. Deep learning techniques are emerging as a powerful tool for this task. The main goal of this work is to review the state-of-the-art for characterizing the deep learning techni...

Convolutional Neural Networks (CNNs) have shown a competitive performance in medical imaging applications, such as image segmentation. However, choosing an existing architecture capable of adapting to a specific dataset is challenging and requires design expertise. Neural Architecture Search (NAS) is employed to overcome these limitations. NAS uses...

Mixed integer nonlinear programming (MINLP) addresses optimization problems that involve continuous and discrete/integer decision variables, as well as nonlinear functions. These problems often exhibit multiple discontinuous feasible parts due to the presence of integer variables. Discontinuous feasible parts can be analyzed as subproblems, some of...

This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and...

The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society for Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence (AI) forum and the main event in the academic life of the country’s growing AI commun...

Semantic segmentation is an important process in computer vision that assigns labels to the pixels of an image to divide it into regions of interest. The most used machine learning model for this problem is the Convolutional Neural Network (CNN), in which high-performance results are obtained, however, they are difficult to understand and explain,...

Wrapper approaches for feature subset selection are computationally intensive because they require training and evaluation of a machine learning algorithm to assess the goodness of a subset of features. This proposal combines the permutational-based differential evolution for feature selection (DE-FSPM) algorithm as a wrapper approach with three in...

Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are char...

Time series classification is a supervised task in the field of temporal data mining. Time series naturally tend to be highly dimensional, requiring the use of reduction techniques such as discretization. eMODiTS is a data-driven method for symbolically discretizing time series, which determines the best scheme by modifying the number of time (word...

Spiking neural networks (SNNs) differentiate themselves from traditional artificial neural networks by modeling the behavior of neurons in a more biologically plausible manner. Consequently, they employ discrete spikes or events to communicate information. Therefore, codifying analog signals into spike trains is a fundamental pre-processing step in...

Medical imaging classification is an area that has taken relevance in recent years due to the capability to support the medical specialist at the time of diagnosis. However, there are different instruments to obtain images from the body, and each body organ is captured differently due to its chemical composition. In this way, there are some difficu...

This study describes the application of four adaptive differential evolution algorithms to generate oblique decision trees. A population of decision trees encoded as real-valued vectors evolves through a global search strategy. Three schemes to create the initial population of the algorithms are applied to reduce the number of redundant nodes (whos...

Many engineering optimization problems fall into the category of Mixed-Integer Nonlinear Programming (MINLP) problems, which combine nonlinear relations, constraint conditions, and different types of variables, including continuous, integer, and/or discrete variables. Solving MINLP problems can be a challenging exploration process since their lands...

This paper presents the gain tuning of an adaptive control law by means of Particle Swarm Optimization (PSO). The restrictions imposed on the particles in the PSO are obtained from the stability analysis of the adaptive control law. In this way, the PSO produces particles associated with optimal gains that simultaneously guarantee closed-loop stabi...

Optimization makes processes, systems, or products more efficient, reliable, and with better outcomes. A popular topic on optimization today is multiobjective bilevel optimization (MOBO). In MOBO, an upper level problem is constrained by the solution of a lower level one. The problem at each level can include multiple conflicting objective function...

One of the main limitations of traditional neural-network-based classifiers is the assumption that all query data are well represented within their training set. Unfortunately, in real-life scenarios, this is often not the case, and unknown class data may appear during testing, which drastically weakens the robustness of the algorithms. For this ty...

Bacterial Vaginosis is a common disease and recurring public health problem. Additionally, this infection can trigger other sexually transmitted diseases. In the medical field, not all possible combinations among the pathogens of a possible case of Bacterial Vaginosis are known to allow a diagnosis at the onset of the disease. It is important to co...

This work deals with the optimal tuning of an Active Disturbance Rejection Controller (ADRC), which is composed of a Luenberger and a Disturbance Observers. The ADRC is applied to the position control of a servo system composed of a DC motor and its associated electronics. The goal of this controller is to reject the disturbances affecting the serv...

Convolutional neural networks (CNN) have been extensively studied and achieved significant progress on a variety of computer vision tasks in recent years. However, the design of their architectures remains challenging due to the computational cost and the number of parameters used. Neuroevolution has offered various evolutionary algorithms to provi...

This paper presents a comparative study of the performance of an unsupervised feature selection method using three evaluation metrics. In the existing literature, various metrics are used to guide the search for a better feature subset and evaluate the resulting data clusterization. Still, there is no well-established path for the unsupervised wrap...

Breast cancer has become a global health problem, ranking first in incidences and fifth in mortality in women around the world. In Mexico, the first cause of death in women is breast cancer. This work uses deep learning techniques to discriminate between healthy and breast cancer patients, based on the banding patterns obtained from the Western Blo...

In this paper, three metaheuristic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) are compared in terms of minimizing the total owning cost (TOC) of the active part of a three-phase shell-type distribution transformer. The three methods use six inputs: power rating, primary voltag...

In multi-label classification, each instance could be assigned multiple labels at the same time. In such a situation, the relationships between labels and the class imbalance are two serious issues that should be addressed. Despite the important number of existing multi-label classification methods, the widespread class imbalance among labels has n...

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Meta-heuristic optimization algorithms quickly lose diversity in such scenarios and get trapped in local optima. In this work, we propose an Estimation of Distribution Algorithm (EDA) with two mod...

The conventional methods of parameter estimation in transformers, such as the open-circuit and short-circuit tests, are not always available, especially when the transformer is already in operation and its disconnection is impossible. Therefore, alternative (non-interruptive) methods of parameter estimation have become of great importance. In this...

The Grouping Genetic Algorithm (GGA) is an extension to the standard Genetic Algorithm that uses a group-based representation scheme and variation operators that work at the group-level. This metaheuristic is one of the most used to solve combinatorial optimization grouping problems. Its optimization process consists of different components, althou...

Most real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. This presented study provides a novel analysis of scholarl...

In the software development cycle, the testing stage is very important to ensure the quality of the software, but it is an expensive activity. Software systems are becoming more complex and therefore difficult to test. Because of this, different optimization approaches such as bioinspired algorithms are widely used in various levels of testing. Stu...

Several Artificial Intelligence based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority towards solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization problems. The search behavior...

This work proposes a new Particle Swarm Optimization (PSO) algorithm specifically designed for parameter identification of physical systems. The key feature of the proposed algorithm is that it takes into consideration the Spectral Richness of the signal used for exciting the system during the identification procedure. The Spectral Richness is esse...

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary Algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an Estimation of Distribution Algorithm (EDA) is...

The one-dimensional Bin Packing Problem (1D-BPP) is a classical NP-hard problem in combinatorial optimization with an extensive number of industrial and logistic applications, considered intractable because it demands a significant amount of resources for its solution. The Grouping Genetic Algorithm with Controlled Gene Transmission (GGA-CGT) is on...

This presented study provides a novel analysis of scholarly literature on constraint handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals, keywords, authors, and articles. The paper reviews the main ideas of the most state-of-the-art constraint handling techniques in multi-...

Many problems of practical and theoretical importance within the fields of Artificial Intelligence and Operations Research are combinatorial. Combinatorial optimization problems consist of finding values for discrete variables that meet certain conditions and maximize (or minimize) an objective function. Usually, the problems with these characteris...

Several real optimization problems are very difficult, and their optimal solutions cannot be found with a traditional method. Moreover, for some of these problems, the large number of decision variables is a major contributing factor to their complexity; they are known as Large-Scale Optimization Problems, and various strategies have been proposed...

This work presents a study about a special class of infeasible solutions called here as pseudo-feasible solutions in bilevel optimization. This work is focused on determining how such solutions can affect the performance of an evolutionary algorithm. After its formal definition, and based on theoretical results, two conditions to detect and deal wi...

In the development of quality software, critical decisions related to planning, estimating, and managing resources are bound to the correct and timely identification of the system needs. In particular, the process of classifying this customer input into software requirements categories tends to become tedious and error-prone when it comes to large-...

Requirements classification is a task commonly made by the human. This fact makes the process error-prone and expensive in a matter of time and effort. This study aims to classify non-functional requirements using a Shallow Artificial Neural Network to support the requirements classification while analyzing its architectural features. We used an ex...

This paper proposes the tuning approach of the event-triggered controller (ETCTA) for the robotic system stabilization task where the reduction of the stabilization error and the data broadcasting of the control update are simultaneously considered. This approach is stated as a dynamic optimization problem, and the best controller parameters are ob...

A cooperative coevolutionary framework can improve the performance of optimization algorithms on large-scale problems. In this paper, we propose a new Cooperative Coevolutionary algorithm to improve our preliminary work, FuzzyPSO2. This new proposal, called CCFPSO, uses the random grouping technique that changes the size of the subcomponents in eac...

The induction of decision trees is a widely-used approach to build classification models that guarantee high performance and expressiveness. Since a recursive-partitioning strategy guided for some splitting criterion is commonly used to induce these classifiers, overfitting, attribute selection bias, and instability to small training set changes ar...

Software requirements classification is a human-intensive task performed during the requirements analysis phase in software development. This literature review analyzes the state-of-the-art of the classification of software requirements using Artificial Neural Networks. Fourteen articles were selected to conduct the review. Sixteen different techni...

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimize...

This work presents a proposal for the automated parameter tuning problem (APTP) modeled as a bilevel optimization problem. Different definitions and theoretical results are given in order to formalize the APTP in the context of this hierarchical optimization problem. The obtained bilevel optimization problem is solved via a population-based algorit...

Cervical cancer represents the fourth cause of death in women worldwide. One of the efforts to decrease this mortality has focused on implementing automatic tools for supporting the experts in diagnosing this illness. In this work, eMODiTS was implemented to explore its performance in this particular domain. A comparison among the most used symboli...

The increasing production of temporal data, especially time series, has motivated valuable knowledge to understand phenomena or for decision-making. As the availability of algorithms to process data increases, the problem of choosing the most suitable one becomes more prevalent. This problem is known as the Full Model Selection (FMS), which consist...

In this article we propose a special kind of Neuroevolution, called NeuroEvolution of Augmenting Topologies (NEAT), which is based on a genetic algorithm, that is then used to generate an artificial neural network to analyze tweets written in Mexican Spanish, and then labeling them as positive, negative and neutral. Classification performance of ne...

Convolutional Neural Networks have shown outstanding results in different application tasks. However, the best performance is obtained when customized Convolutional Neural Networks architectures are designed, which is labor-intensive and requires highly specialized knowledge. Over three decades, Neuroevolution has studied the application of Evoluti...

Cloud computing provides effective ways to rapidly provision computing resources over the Internet. For a better management of resource provisioning, the system requires to predict service-level agreements (SLAs) such as virtual machine (VM) startup times under various conditions of computing resources. The VM startup time is an important SLA param...

This paper presents an extended empirical comparison of the Advanced jSO (AJSO), an algorithm adapted to solve smart grid optimization problems. An additional algorithm was considered for comparison purposes and a suitable statistical test validation was also added. Furthermore, a convergence analysis was included to give insights about the on-line...

Grouping problems are combinatorial optimization problems, most of them NP-hard, related to the partition of a set of items into different groups or clusters. Given their numerous real-world applications, different solution approaches have been presented to deal with the high complexity of NP-hard grouping problems. However, the Grouping Genetic Al...

This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques....

This paper presents distance-based immune generalised differential evolution (DIGDE), an improved algorithmic approach to tackle dynamic multi-objective optimisation problems (DMOPs). Its novelty is using the inverted generational distance (IGD) as an indicator in its selection mechanism to guide the search. DIGDE is based on the immune generalised...

This work proposes Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given BN structure encodes assumptions about conditional independencies among the attributes and will result in error if they do...

This paper presents a novel approach based on the combination of the Modified Brain Storm Optimization algorithm (MBSO) with a simplified version of the Constraint Consensus method as special operator to solve constrained numerical optimization problems. Regarding the special operator, which aims to reach the feasible region of the search space, th...

Grouping problems are combinatorial optimization problems, most of them NP-hard, related to the partition of a set of items into different groups or clusters. Given their numerous real-world applications, different solution approaches have been presented to deal with the high complexity of NP-hard grouping problems. However, the Grouping Genetic Al...

This study presents an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve robust optimization over time problems with a survival time approach to analyze its viability and performance capacity of solving problems in dynamic environments. A set of instances with four different dynamics, ge...

We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Non-dominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is...

This work proposes Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Although Discriminative Parameter Learning algorithms have been proposed, to the best of the authors' knowledge, a metaheuristic app...

An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fe...

Time series discretization is a technique commonly used to tackle time series classification problems. This manuscript presents an enhanced multi-objective approach for the symbolic discretization of time series called eMODiTS. The method proposed uses a different breakpoints vector, defined per each word segment, to increase the search space of th...

We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is b...

This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better distributed Pareto front. The considered power system is composed...

The identification of subnetworks of interest - or active modules - by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in multiplex biological networks. MOGAMUN opti...

Multi-objective optimization has been adopted in many engineering problems where a set of requirements must be met to generate successful applications. Among them, there are the tuning problems from control engineering, which are focused on the correct setting of the controller parameters to properly govern complex dynamic systems to satisfy desire...

Grouping problems are a special type of combinatorial optimization problems that have gained great relevance because of their numerous real-world applications. The solution process required by some grouping problems represents a high complexity, and currently, there is no algorithm to find the optimal solution efficiently in the worst case. Consequ...

User Interface Design Patterns (UIDPs) improve the interaction between users and e-applications through the use of interfaces with a suitable and intuitive navigability without restrictions on the size of the screen to show the content. Nowadays, UIDPs are frequently used in the development of new mobile apps. In fact, mobile apps are ubiquitous: i...

This paper describes a permutational-based Differential Evolution algorithm implemented in a wrapper scheme to find a feature subset to be applied in the construction of a near-optimal classifier. In this approach, the relevance of a feature chosen to build a better classifier is represented through its relative position in an integer-valued vector...

In the modern era, the applications of the wireless network increase rapidly in the forms of several variations. Wireless Body Area Sensor Network (WBASN) is one of the variations of the wireless network. The purpose of this network is to monitor and detect several characteristics of the body and transmit into the proper destination. This is an int...

Memetic approaches are composed of three general processes, a global optimizer, a set of local-search operators, and a coordination mechanism; which are defined depending on the problem to be optimized. For constrained optimization problems (COPs), memetic algorithms require the incorporation of a constraint handler that guides the search to the fe...

Nature-inspired optimization algorithms are meta-heuristics that mimic nature for solving optimization problems. Many optimization problems are constrained and have a bounded search space from which some solution vectors leave when the variation operators are applied. Therefore, the use of boundary constraint-handling methods (BCHM) is necessary in...

Cooperative Co-evolutionary algorithms are very popular to solve large-scale problems. A significant part of these algorithms is the decomposition of the problems according to the variables interaction. In this paper, an approach based on a memetic scheme, where its local stage (and not the global stage) is guided by the decomposition method (Local...

The efficient speed regulation of four-bar mechanisms is required for many industrial processes. These mechanisms are hard to control due to the highly nonlinear behavior and the presence of uncertainties or disturbances. In this paper, different Pareto-front approximation search approaches in the adaptive controller tuning based on online multiobj...

Corner detection represents one of the most important steps to identify features in images. Due to their powerful local processing capabilities, Cellular Nonlinear/Neural Networks (CNN) are commonly utilized in image processing applications such as image edge detection, image encoding and image hole filling. CNN perform well for locating corner fea...

This chapter presents an empirical comparison of six deterministic parameter control schemes based on a sinusoidal behavior that are incorporated into a differential evolution algorithm called “Differential Evolution with Combined Variants” (DECV) to solve constrained numerical optimization problems. Besides, the feasibility rules and the ε-constra...