Efrén Mezura-Montes

Efrén Mezura-Montes
Universidad Veracruzana | UV · Departamento de Inteligencia Artificial

Doctor of Philosophy

About

203
Publications
35,382
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6,984
Citations
Introduction
Dr. Efrén Mezura-Montes works on the design and study of nature-inspired meta-heuristics to solve complex optimization problems in different domains like engineering, machine learning, image processing, knowledge discovery in data, among others. He specializes also in constrained optimization.

Publications

Publications (203)
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
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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...
Article
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...
Article
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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...
Article
Full-text available
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...
Article
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...
Article
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...
Chapter
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...
Article
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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...
Article
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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...
Chapter
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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...
Article
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...
Book
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....
Article
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...
Article
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...
Article
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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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
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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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
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...
Article
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...
Chapter
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...
Conference Paper
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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...
Article
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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...
Article
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...
Article
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...
Article
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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...
Chapter
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...
Chapter
Full-text available
Physical phenomena have been the inspiration for proposing different optimization methods such as electro-search algorithm, central force optimization, and charged system search among others. This work presents a new optimization algorithm based on some principles from physics and mechanics, which is called Evolutionary Centers Algorithm (ECA). We...
Article
In this work, a graph-theory based approach for representing planar mechanisms is presented, the Santiago-Portilla Method (SPM). From the corresponding adjacency matrix, SPM generates an extended matrix containing the complete characterization of a planar mechanism, including all the information about both topology and geometry. This matrix represe...
Article
Many real-world constrained numerical optimization problems are defined through expensive models. An alternative to solve them is by using evolutionary algorithms. However, they usually require a considerable number of evaluations to get a competitive solution, and such task needs a high computational effort. This disadvantage can be tackled with s...
Article
Full-text available
The adaptive design of the control system for a direct current motor is solved by proposing differential evolution based control adaptation (DEBAC). From the comparison of two differential evolution variants with two constraint-handling techniques, a competitive algorithm based on arithmetic crossover and a set of feasibility rules is obtained. In...
Article
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Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time. There are studies about the effect of constrained handling techniques in static optimization problems. However, there lacks any substantial study in the behavior of the most popular con...
Article
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An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ + Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to...
Article
In this paper, an Immune Generalized Differential Evolution 3 (Immune GDE3) algorithm to solve dynamic multi-objective optimization problems (DMOPs) is empirically analyzed. Three main issues of the algorithm are explored: (1) the general performance of Immune GDE3 in comparison with other well-known algorithms, (2) its sensitivity to different cha...
Article
Full-text available
In this paper, a concurrent structure-control design is applied to a five-bar parallel robot in order to minimize the tracking error in a high-speed task. The high power necessary to perform the task leads to a challenging problem in handling a physical constraint on the maximum torque as well as on the structure-control design parameters. Three co...
Article
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This paper introduces an adaptive local search coordination for a multimeme Differential Evolution to constrained numerical optimization problems. The proposed approach associates a pool of direct local search operators within the standard Differential Evolution. The coordination mechanism consists of a probabilistic method based on a cost-benefit...
Article
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The multi-objective clustering with automatic determination of the number of clusters (MOCK) approach is improved in this work by means of an empirical comparison of three multi-objective evolutionary algorithms added to MOCK instead of the original algorithm used in such approach. The results of two different experiments using seven real data sets...