Juan Martín Carpio Valadez - Academia.edu (original) (raw)

Papers by Juan Martín Carpio Valadez

Research paper thumbnail of Comportamiento Sinérgico En Hiperheurística de Selección para la Solución de los Problemas del Agente Viajero

Programación Matemática y Software

En este trabajo se muestra el comportamiento sinérgico que se produce en la implementación de una... more En este trabajo se muestra el comportamiento sinérgico que se produce en la implementación de una Hiperheurística de selección aplicada al problema del agente viajero (TSP, por sus siglas en inglés). Como órgano rector de la Hiperheurística se utilizó un Algoritmo Genético, y un conjunto de 5 heurísticas de bajo nivel. Para hacer las pruebas se utilizaron instancias de entrenamiento del estado del arte para TSP, y para el análisis de resultados, se hizo una comparación del mejor genotipo obtenido del entrenamiento de la combinación de las heurísticas, contra genotipos que contienen un solo tipo de heurística analizados desde un enfoque de optimización. En las pruebas estadísticas se utilizó como representante estadístico la mediana obtenida de dichos experimentos. Se presentan la explicación del entrenamiento fuera de línea de la Hiperheurística y los resultados que muestran que la hiperheurística es capaz de mejorar los resultados de las heurísticas aplicadas individualmente.

Research paper thumbnail of Application of Strategies on NSGAII for Searching of Optimal Solutions to the Car Sequencing Problem

ECORFAN Journal-Democratic Republic of Congo

One of the main conflicts in a car production plant is to deliver the orders received daily in a ... more One of the main conflicts in a car production plant is to deliver the orders received daily in a timely manner, which are not uniform and involve a large amount of human and material resources. The car sequencing problem is a NP-Hard problem that consists of finding the sequence of cars that minimizes the number of constraint violations in an assembly line. The problem can be approached from a mono-objective or multi-objective point of view. The objective of this paper is to treat a case study of this problem, presented at ROADEF 2005, from the multi-objective Pareto approach, taking the NSGAII algorithm as a basis for a proposal scheme and verifying its feasibility. A systematic and general improvement of the quality of the final Pareto fronts is verified, and the results of the implementation of a strategy scheme that consists of the initialization of the population guided by local search, and specialized crossover and mutation operators are reported. These results allow us to giv...

Research paper thumbnail of Configuration Module for Treating Design Anomalies in Databases for a Natural Language Interface to Databases

Natural language interfaces for databases (NLIDBs) are tools that allow obtaining information fro... more Natural language interfaces for databases (NLIDBs) are tools that allow obtaining information from a database (DB) through natural language queries. Currently, domain-independent NLIDBs (interfaces capable of working with several DBs) have not had a favorable performance due to the complexity for devising an approach that can solve all the problems that occur in NLIDBs. Another important problem is the existence of design anomalies in DBs, which have not been addressed in the approaches proposed by various authors. This chapter describes the implementation of a module for dealing with four design anomalies: absence of primary and foreign keys, use of surrogate keys, Columns for storing aggregate function calculations, and columns repeated in two or more tables. This module was implemented in an experimental NLIDB. The purpose of this is to allow the NLIDB to be configured so that it perceives an anomalous DB as a DB without design anomalies so the NLIDB can function correctly. Exper...

Research paper thumbnail of Studying Grammatical Evolution's Mapping Processes for Symbolic Regression Problems

Grammatical Evolution (GE) is a variant of Genetic Programming (GP) that uses a BNF-grammar to cr... more Grammatical Evolution (GE) is a variant of Genetic Programming (GP) that uses a BNF-grammar to create syntactically correct solutions. GE is composed of the following components: the Problem Instance, the BNF-grammar (BNF), the Search Engine (SE) and the Mapping Process (MP). GE allows creating a distinction between the solution and search spaces using an MP and the BNF to translate from genotype to phenotype, that avoids invalid solutions that can be obtained with GP. One genotype can generate different phenotypes using a different MP. There exist at least three MPs widely used in the art-state: Depth-first (DF), Breadth-first (BF) and \( \pi \)Grammatical Evolution (piGE). In the present work DF, BF, and piGE have been studied in the Symbolic Regression Problem. The results were compared using a statistical test to determine which MP gives the best results.

Research paper thumbnail of Combinatorial Designs on Constraint Satisfaction Problem (VRP)

Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, 2020

The constraint satisfaction problems (CSP) often show great complexity and require a combination ... more The constraint satisfaction problems (CSP) often show great complexity and require a combination of heuristic methods and combinatorial search to be solved in a reasonable time. Therefore, they are of particular importance in the area of intelligent systems. A proposal of a methodology for solving CSP problems is presented, in which the characteristics of combinatorial designs based on algebraic structures, such as Mutually Orthogonal Latin Squares, are exploited in the search for solutions (answers) to a CSP problem. The proposal and the set of heuristics associated with the combinatorial design are evaluated, looking for the pair of heuristics with the best performance in the set of artificial instances of the vehicle routing problem (VRP). The results show the usefulness of the combinatorial designs to find solutions that resolve artificial instances and support the feasibility to extend its application on instances of the state-of-the-art and later on different problem domains.

Research paper thumbnail of Evolutionary Design of Problem-Adapted Image Descriptors for Texture Classification

IEEE Access, 2018

Effective texture classification requires image descriptors capable of efficiently detecting, ext... more Effective texture classification requires image descriptors capable of efficiently detecting, extracting, and describing the most relevant information in the images, so that, for instance, different texture classes can be distinguished despite image distortions such as varying illuminations, viewpoints, scales, and rotations. Designing such an image descriptor is a challenging task that typically involves the intervention of human experts. In this paper, a general method to automatically design effective image descriptors is proposed. Our method is based on grammatical evolution and, using a set of example images from a texture classification problem and a classification algorithm as inputs, generates problem-adapted image descriptors that achieve very competitive classification results. Our method is tested on five well-known texture data sets with different number of classes and image distortions to prove its effectiveness and robustness. Our classification results are statistically compared against those obtained by means of six popular hand-crafted texture descriptors in the state of the art. This statistical analysis shows that our evolutionarily designed descriptors outperform most of those designed by human experts.

Research paper thumbnail of Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis

IEEE Access, 2018

Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. ... more Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.

Research paper thumbnail of Integer Linear Programming Formulation and Exact Algorithm for Computing Pathwidth

Studies in Computational Intelligence, 2016

Computing the Pathwidth of a graph is the problem of finding a linear ordering of the vertices su... more Computing the Pathwidth of a graph is the problem of finding a linear ordering of the vertices such that the width of its corresponding path decomposition is minimized. This problem has been proven to be NP-hard. Currently, some of the best exact methods for generic graphs can be found in the mathematical software project called SageMath. This project provides an integer linear programming model (IPSAGE) and an enumerative algorithm (EASAGE), which is exponential in time and space. The algorithm EASAGE uses an array whose size grows exponentially with respect to the size of the problem. The purpose of this array is to improve the performance of the algorithm. In this chapter we propose two exact methods for computing pathwidth. More precisely, we propose a new integer linear programming formulation (IPPW) and a new enumerative algorithm (BBPW). The formulation IPPW generates a smaller number of variables and constraints than IPSAGE. The algorithm BBPW overcomes the exponential space requirement by using a last-in-first-out stack. The experimental results showed that, in average, IPPW reduced the number of variables by 33.3 % and the number of constraints by 64.3 % with respect to IPSAGE. This reduction of variables and constraints allowed IPPW to save approximately 14.9 % of the computing time of IPSAGE. The results also revealed that BBPW achieved a remarkable use of memory with respect to EASAGE. In average, BBPW required 2073 times less amount of memory than EASAGE for solving the same set of instances.

Research paper thumbnail of Comparison of Optimization Techniques for Modular Neural Networks Applied to Human Recognition

Studies in Computational Intelligence, 2016

In this paper a comparison of optimization techniques for a Modular Neural Network (MNN) with a g... more In this paper a comparison of optimization techniques for a Modular Neural Network (MNN) with a granular approach is presented. A Hierarchical Genetic Algorithm, a Firefly Algorithm (FA), and a Grey Wolf Optimizer are developed to perform a comparison of results. These algorithms design optimal MNN architectures, where their main task is the optimization of some parameters of MNN such as, number of sub modules, percentage of information for the training phase and number of hidden layers (with their respective number of neurons) for each sub module and learning algorithm. The MNNs are applied to human recognition based on iris biometrics, where a benchmark database is used to perform the comparison, having as objective function in each optimization algorithm the minimization of the error of recognition.

Research paper thumbnail of Comparing Grammatical Evolution’s Mapping Processes on Feature Generation for Pattern Recognition Problems

Studies in Computational Intelligence, 2016

Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. In GE, a Mapping Proce... more Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. In GE, a Mapping Process (MP) and a Backus–Naur Form grammar (defined in the problem context) are used to transform each individual’s genotype into its phenotype form (functional representation). There are several MPs proposed in the state-of-the-art, each of them defines how the individual’s genes are used to build its phenotype form. This paper compares two MPs: the Depth-First standard map and the Position Independent Grammatical Evolution (πGE). The comparison was performed using as use case the problem of the selection and generation of features for pattern recognition problems. A Wilcoxon Rank-Sum test was used to compare and validate the results of the different approaches.

Research paper thumbnail of Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Studies in Computational Intelligence, 2016

In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Prog... more In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.

Research paper thumbnail of A heterogeneous cellular processing algorithm for minimizing the power consumption in wireless communications systems

Computational Optimization and Applications, 2015

In this paper, the NP-hard problem of minimizing power consumption in wireless communications sys... more In this paper, the NP-hard problem of minimizing power consumption in wireless communications systems is approached. In the literature, several metaheuristic approaches have been proposed to solve it. Currently a homogeneous cellular processing algorithm and a GRASP algorithm hybridized with path-relinking are considered the state of the art algorithms. The main contribution of this paper is the analysis of five main characteristics for a heterogeneous cellular processing algorithm, based on scatter search and GRASP. A series of computational experiments with standard instances were carried out to assess the impact of each one of these characteristics. Among the main analyses we found particularly interesting a time reduction by 74.24%, produced by the stagnation detection characteristic. Also the communication characteristic improves the quality of the solutions by 24.73%. The B Héctor Joaquín Fraire Huacuja automatas2002@yahoo.com.mx J. David Terán-Villanueva david_teran01@yahoo.com.mx Juan Martín Carpio Valadez jmcarpio61@hotmail.com Rodolfo Pazos Rangel r_pazos_r@yahoo.com.mx Héctor José Puga Soberanes pugahector@yahoo.com José A. Martínez Flores jose.mtz@itcm.edu.mx 1 Instituto Tecnológico de León (ITL), Avenida Tecnológico s/No, C.P. 37290, León, Gto, Mexico 2 Instituto Tecnológico de Ciudad Madero (ITCM), Av. 1o. de Mayo s/No esq. Sor Juana Inés de la Cruz, C.P. 89440, Ciudad Madero, Tam, Mexico

Research paper thumbnail of Generic Memetic Algorithm for Course Timetabling ITC2007

Studies in Computational Intelligence, 2014

Course timetabling is an important and recurring administrative activity in most educational inst... more Course timetabling is an important and recurring administrative activity in most educational institutions. This chapter describes an automated configuration of a generic memetic algorithm to solving this problem. This algorithm shows competitive results on well-known instances compared against top participants of the most recent International ITC2007 Timetabling Competition. Importantly, our study illustrates a case where generic algorithms with increased autonomy and generality achieve competitive performance against human designed problem-specific algorithms.

Research paper thumbnail of Comparing Metaheuristic Algorithms on the Training Process of Spiking Neural Networks

Studies in Computational Intelligence, 2014

Spiking Neural Networks are considered as the third generation of Artificial Neural Networks. In ... more Spiking Neural Networks are considered as the third generation of Artificial Neural Networks. In these networks, spiking neurons receive/send the information by timing of events (spikes) instead by the spike rate; as their predecessors do. Spikeprop algorithm, based on gradient descent, was developed as learning rule for training SNNs to solve pattern recognition problems; however this algorithm trends to be trapped in local minima and has several limitations. For dealing with the supervised learning on Spiking Neural Networks without the drawbacks of Spikeprop, several metaheuristics such as: Evolutionary Strategy, Particle Swarm Optimization, have been used to tune the neural parameters. This work compares the performance and the impact of some metaheuristics used for training spiking neural networks.

Research paper thumbnail of On the Exact Solution of VSP for General and Structured Graphs: Models and Algorithms

Studies in Computational Intelligence, 2014

ABSTRACT

Research paper thumbnail of Iterated Local Search Algorithm for the Linear Ordering Problem with Cumulative Costs (LOPCC)

Studies in Computational Intelligence, 2010

In this article we approach the linear ordering problem with cumulative costs (LOPCC). Bertacco d... more In this article we approach the linear ordering problem with cumulative costs (LOPCC). Bertacco developed this problem [2] and propose two exact algorithms, due to the complexity of the problem Duarte propose a Tabu algorithm for LOPCC [3] and until now that algorithm is the state of the art. In this ongoing research we propose a set of iterated local search algorithms (ILS) to solve the LOPCC. The experimental evidence shows that the performance of the iterated local search algorithms evaluated have similar quality to the best solution reported and get better efficiency than the reference solution. Also with the local search algorithms we improve the best known solution for 32 instances. Now we are working in developing new algorithms with population metaheuristics.

Research paper thumbnail of Variable Length Number Chains Generation without Repetitions

Studies in Computational Intelligence, 2010

Abstract. Pseudorandom and random numbers generators, plays an important role in solving many rea... more Abstract. Pseudorandom and random numbers generators, plays an important role in solving many real or simulated problems, in different domains such as Scientific Computing, Physics, Chemistry, Computer Science, Artificial Intelli-gence, Chaos, Games theory, ...

Research paper thumbnail of Demodulation of Interferograms of Closed Fringes by Zernike Polynomials using a technique of Soft Computing

Engineering Letters

We present the results about to recover the phase of interferograms of closed fringes by Zernike ... more We present the results about to recover the phase of interferograms of closed fringes by Zernike polynomials using a technique of soft computing, applying genetic algorithms (AG) and using an optimization fitness based with Zernike polynomials, with results very satisfactory.

Research paper thumbnail of A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials

Lecture Notes in Computer Science, 2020

A novel set of moment invariants for pattern recognition applications, which are based on Jacobi ... more A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. This invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. The results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.

Research paper thumbnail of A novel model for optimization of Intelligent Multi-User Visual Comfort System based on soft-computing algorithms

Journal of Ambient Intelligence and Smart Environments, 2021

Intelligent buildings are at the forefront due to its main objective of providing comfort to user... more Intelligent buildings are at the forefront due to its main objective of providing comfort to users and saving energy through intelligent control systems. Intelligent systems have been reported to offer comfort to a single user or averaging the comfort of multiple users without considering that their needs may be different from those of other users. This work defines a versatile model for a multi-user intelligent system that negotiates with the resources of the environment to offer visual comfort to multiple users with different profiles, activities and priorities using soft-computing algorithms. In addition, this model makes use of external lighting to provide the recommended amount of illumination for each user without having to totally depend on artificial lighting, inducing there will be an energy efficiency but without measuring it.

Research paper thumbnail of Comportamiento Sinérgico En Hiperheurística de Selección para la Solución de los Problemas del Agente Viajero

Programación Matemática y Software

En este trabajo se muestra el comportamiento sinérgico que se produce en la implementación de una... more En este trabajo se muestra el comportamiento sinérgico que se produce en la implementación de una Hiperheurística de selección aplicada al problema del agente viajero (TSP, por sus siglas en inglés). Como órgano rector de la Hiperheurística se utilizó un Algoritmo Genético, y un conjunto de 5 heurísticas de bajo nivel. Para hacer las pruebas se utilizaron instancias de entrenamiento del estado del arte para TSP, y para el análisis de resultados, se hizo una comparación del mejor genotipo obtenido del entrenamiento de la combinación de las heurísticas, contra genotipos que contienen un solo tipo de heurística analizados desde un enfoque de optimización. En las pruebas estadísticas se utilizó como representante estadístico la mediana obtenida de dichos experimentos. Se presentan la explicación del entrenamiento fuera de línea de la Hiperheurística y los resultados que muestran que la hiperheurística es capaz de mejorar los resultados de las heurísticas aplicadas individualmente.

Research paper thumbnail of Application of Strategies on NSGAII for Searching of Optimal Solutions to the Car Sequencing Problem

ECORFAN Journal-Democratic Republic of Congo

One of the main conflicts in a car production plant is to deliver the orders received daily in a ... more One of the main conflicts in a car production plant is to deliver the orders received daily in a timely manner, which are not uniform and involve a large amount of human and material resources. The car sequencing problem is a NP-Hard problem that consists of finding the sequence of cars that minimizes the number of constraint violations in an assembly line. The problem can be approached from a mono-objective or multi-objective point of view. The objective of this paper is to treat a case study of this problem, presented at ROADEF 2005, from the multi-objective Pareto approach, taking the NSGAII algorithm as a basis for a proposal scheme and verifying its feasibility. A systematic and general improvement of the quality of the final Pareto fronts is verified, and the results of the implementation of a strategy scheme that consists of the initialization of the population guided by local search, and specialized crossover and mutation operators are reported. These results allow us to giv...

Research paper thumbnail of Configuration Module for Treating Design Anomalies in Databases for a Natural Language Interface to Databases

Natural language interfaces for databases (NLIDBs) are tools that allow obtaining information fro... more Natural language interfaces for databases (NLIDBs) are tools that allow obtaining information from a database (DB) through natural language queries. Currently, domain-independent NLIDBs (interfaces capable of working with several DBs) have not had a favorable performance due to the complexity for devising an approach that can solve all the problems that occur in NLIDBs. Another important problem is the existence of design anomalies in DBs, which have not been addressed in the approaches proposed by various authors. This chapter describes the implementation of a module for dealing with four design anomalies: absence of primary and foreign keys, use of surrogate keys, Columns for storing aggregate function calculations, and columns repeated in two or more tables. This module was implemented in an experimental NLIDB. The purpose of this is to allow the NLIDB to be configured so that it perceives an anomalous DB as a DB without design anomalies so the NLIDB can function correctly. Exper...

Research paper thumbnail of Studying Grammatical Evolution's Mapping Processes for Symbolic Regression Problems

Grammatical Evolution (GE) is a variant of Genetic Programming (GP) that uses a BNF-grammar to cr... more Grammatical Evolution (GE) is a variant of Genetic Programming (GP) that uses a BNF-grammar to create syntactically correct solutions. GE is composed of the following components: the Problem Instance, the BNF-grammar (BNF), the Search Engine (SE) and the Mapping Process (MP). GE allows creating a distinction between the solution and search spaces using an MP and the BNF to translate from genotype to phenotype, that avoids invalid solutions that can be obtained with GP. One genotype can generate different phenotypes using a different MP. There exist at least three MPs widely used in the art-state: Depth-first (DF), Breadth-first (BF) and \( \pi \)Grammatical Evolution (piGE). In the present work DF, BF, and piGE have been studied in the Symbolic Regression Problem. The results were compared using a statistical test to determine which MP gives the best results.

Research paper thumbnail of Combinatorial Designs on Constraint Satisfaction Problem (VRP)

Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, 2020

The constraint satisfaction problems (CSP) often show great complexity and require a combination ... more The constraint satisfaction problems (CSP) often show great complexity and require a combination of heuristic methods and combinatorial search to be solved in a reasonable time. Therefore, they are of particular importance in the area of intelligent systems. A proposal of a methodology for solving CSP problems is presented, in which the characteristics of combinatorial designs based on algebraic structures, such as Mutually Orthogonal Latin Squares, are exploited in the search for solutions (answers) to a CSP problem. The proposal and the set of heuristics associated with the combinatorial design are evaluated, looking for the pair of heuristics with the best performance in the set of artificial instances of the vehicle routing problem (VRP). The results show the usefulness of the combinatorial designs to find solutions that resolve artificial instances and support the feasibility to extend its application on instances of the state-of-the-art and later on different problem domains.

Research paper thumbnail of Evolutionary Design of Problem-Adapted Image Descriptors for Texture Classification

IEEE Access, 2018

Effective texture classification requires image descriptors capable of efficiently detecting, ext... more Effective texture classification requires image descriptors capable of efficiently detecting, extracting, and describing the most relevant information in the images, so that, for instance, different texture classes can be distinguished despite image distortions such as varying illuminations, viewpoints, scales, and rotations. Designing such an image descriptor is a challenging task that typically involves the intervention of human experts. In this paper, a general method to automatically design effective image descriptors is proposed. Our method is based on grammatical evolution and, using a set of example images from a texture classification problem and a classification algorithm as inputs, generates problem-adapted image descriptors that achieve very competitive classification results. Our method is tested on five well-known texture data sets with different number of classes and image distortions to prove its effectiveness and robustness. Our classification results are statistically compared against those obtained by means of six popular hand-crafted texture descriptors in the state of the art. This statistical analysis shows that our evolutionarily designed descriptors outperform most of those designed by human experts.

Research paper thumbnail of Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis

IEEE Access, 2018

Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. ... more Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.

Research paper thumbnail of Integer Linear Programming Formulation and Exact Algorithm for Computing Pathwidth

Studies in Computational Intelligence, 2016

Computing the Pathwidth of a graph is the problem of finding a linear ordering of the vertices su... more Computing the Pathwidth of a graph is the problem of finding a linear ordering of the vertices such that the width of its corresponding path decomposition is minimized. This problem has been proven to be NP-hard. Currently, some of the best exact methods for generic graphs can be found in the mathematical software project called SageMath. This project provides an integer linear programming model (IPSAGE) and an enumerative algorithm (EASAGE), which is exponential in time and space. The algorithm EASAGE uses an array whose size grows exponentially with respect to the size of the problem. The purpose of this array is to improve the performance of the algorithm. In this chapter we propose two exact methods for computing pathwidth. More precisely, we propose a new integer linear programming formulation (IPPW) and a new enumerative algorithm (BBPW). The formulation IPPW generates a smaller number of variables and constraints than IPSAGE. The algorithm BBPW overcomes the exponential space requirement by using a last-in-first-out stack. The experimental results showed that, in average, IPPW reduced the number of variables by 33.3 % and the number of constraints by 64.3 % with respect to IPSAGE. This reduction of variables and constraints allowed IPPW to save approximately 14.9 % of the computing time of IPSAGE. The results also revealed that BBPW achieved a remarkable use of memory with respect to EASAGE. In average, BBPW required 2073 times less amount of memory than EASAGE for solving the same set of instances.

Research paper thumbnail of Comparison of Optimization Techniques for Modular Neural Networks Applied to Human Recognition

Studies in Computational Intelligence, 2016

In this paper a comparison of optimization techniques for a Modular Neural Network (MNN) with a g... more In this paper a comparison of optimization techniques for a Modular Neural Network (MNN) with a granular approach is presented. A Hierarchical Genetic Algorithm, a Firefly Algorithm (FA), and a Grey Wolf Optimizer are developed to perform a comparison of results. These algorithms design optimal MNN architectures, where their main task is the optimization of some parameters of MNN such as, number of sub modules, percentage of information for the training phase and number of hidden layers (with their respective number of neurons) for each sub module and learning algorithm. The MNNs are applied to human recognition based on iris biometrics, where a benchmark database is used to perform the comparison, having as objective function in each optimization algorithm the minimization of the error of recognition.

Research paper thumbnail of Comparing Grammatical Evolution’s Mapping Processes on Feature Generation for Pattern Recognition Problems

Studies in Computational Intelligence, 2016

Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. In GE, a Mapping Proce... more Grammatical Evolution (GE) is a grammar-based form of Genetic Programming. In GE, a Mapping Process (MP) and a Backus–Naur Form grammar (defined in the problem context) are used to transform each individual’s genotype into its phenotype form (functional representation). There are several MPs proposed in the state-of-the-art, each of them defines how the individual’s genes are used to build its phenotype form. This paper compares two MPs: the Depth-First standard map and the Position Independent Grammatical Evolution (πGE). The comparison was performed using as use case the problem of the selection and generation of features for pattern recognition problems. A Wilcoxon Rank-Sum test was used to compare and validate the results of the different approaches.

Research paper thumbnail of Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Studies in Computational Intelligence, 2016

In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Prog... more In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.

Research paper thumbnail of A heterogeneous cellular processing algorithm for minimizing the power consumption in wireless communications systems

Computational Optimization and Applications, 2015

In this paper, the NP-hard problem of minimizing power consumption in wireless communications sys... more In this paper, the NP-hard problem of minimizing power consumption in wireless communications systems is approached. In the literature, several metaheuristic approaches have been proposed to solve it. Currently a homogeneous cellular processing algorithm and a GRASP algorithm hybridized with path-relinking are considered the state of the art algorithms. The main contribution of this paper is the analysis of five main characteristics for a heterogeneous cellular processing algorithm, based on scatter search and GRASP. A series of computational experiments with standard instances were carried out to assess the impact of each one of these characteristics. Among the main analyses we found particularly interesting a time reduction by 74.24%, produced by the stagnation detection characteristic. Also the communication characteristic improves the quality of the solutions by 24.73%. The B Héctor Joaquín Fraire Huacuja automatas2002@yahoo.com.mx J. David Terán-Villanueva david_teran01@yahoo.com.mx Juan Martín Carpio Valadez jmcarpio61@hotmail.com Rodolfo Pazos Rangel r_pazos_r@yahoo.com.mx Héctor José Puga Soberanes pugahector@yahoo.com José A. Martínez Flores jose.mtz@itcm.edu.mx 1 Instituto Tecnológico de León (ITL), Avenida Tecnológico s/No, C.P. 37290, León, Gto, Mexico 2 Instituto Tecnológico de Ciudad Madero (ITCM), Av. 1o. de Mayo s/No esq. Sor Juana Inés de la Cruz, C.P. 89440, Ciudad Madero, Tam, Mexico

Research paper thumbnail of Generic Memetic Algorithm for Course Timetabling ITC2007

Studies in Computational Intelligence, 2014

Course timetabling is an important and recurring administrative activity in most educational inst... more Course timetabling is an important and recurring administrative activity in most educational institutions. This chapter describes an automated configuration of a generic memetic algorithm to solving this problem. This algorithm shows competitive results on well-known instances compared against top participants of the most recent International ITC2007 Timetabling Competition. Importantly, our study illustrates a case where generic algorithms with increased autonomy and generality achieve competitive performance against human designed problem-specific algorithms.

Research paper thumbnail of Comparing Metaheuristic Algorithms on the Training Process of Spiking Neural Networks

Studies in Computational Intelligence, 2014

Spiking Neural Networks are considered as the third generation of Artificial Neural Networks. In ... more Spiking Neural Networks are considered as the third generation of Artificial Neural Networks. In these networks, spiking neurons receive/send the information by timing of events (spikes) instead by the spike rate; as their predecessors do. Spikeprop algorithm, based on gradient descent, was developed as learning rule for training SNNs to solve pattern recognition problems; however this algorithm trends to be trapped in local minima and has several limitations. For dealing with the supervised learning on Spiking Neural Networks without the drawbacks of Spikeprop, several metaheuristics such as: Evolutionary Strategy, Particle Swarm Optimization, have been used to tune the neural parameters. This work compares the performance and the impact of some metaheuristics used for training spiking neural networks.

Research paper thumbnail of On the Exact Solution of VSP for General and Structured Graphs: Models and Algorithms

Studies in Computational Intelligence, 2014

ABSTRACT

Research paper thumbnail of Iterated Local Search Algorithm for the Linear Ordering Problem with Cumulative Costs (LOPCC)

Studies in Computational Intelligence, 2010

In this article we approach the linear ordering problem with cumulative costs (LOPCC). Bertacco d... more In this article we approach the linear ordering problem with cumulative costs (LOPCC). Bertacco developed this problem [2] and propose two exact algorithms, due to the complexity of the problem Duarte propose a Tabu algorithm for LOPCC [3] and until now that algorithm is the state of the art. In this ongoing research we propose a set of iterated local search algorithms (ILS) to solve the LOPCC. The experimental evidence shows that the performance of the iterated local search algorithms evaluated have similar quality to the best solution reported and get better efficiency than the reference solution. Also with the local search algorithms we improve the best known solution for 32 instances. Now we are working in developing new algorithms with population metaheuristics.

Research paper thumbnail of Variable Length Number Chains Generation without Repetitions

Studies in Computational Intelligence, 2010

Abstract. Pseudorandom and random numbers generators, plays an important role in solving many rea... more Abstract. Pseudorandom and random numbers generators, plays an important role in solving many real or simulated problems, in different domains such as Scientific Computing, Physics, Chemistry, Computer Science, Artificial Intelli-gence, Chaos, Games theory, ...

Research paper thumbnail of Demodulation of Interferograms of Closed Fringes by Zernike Polynomials using a technique of Soft Computing

Engineering Letters

We present the results about to recover the phase of interferograms of closed fringes by Zernike ... more We present the results about to recover the phase of interferograms of closed fringes by Zernike polynomials using a technique of soft computing, applying genetic algorithms (AG) and using an optimization fitness based with Zernike polynomials, with results very satisfactory.

Research paper thumbnail of A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials

Lecture Notes in Computer Science, 2020

A novel set of moment invariants for pattern recognition applications, which are based on Jacobi ... more A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. This invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. The results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.

Research paper thumbnail of A novel model for optimization of Intelligent Multi-User Visual Comfort System based on soft-computing algorithms

Journal of Ambient Intelligence and Smart Environments, 2021

Intelligent buildings are at the forefront due to its main objective of providing comfort to user... more Intelligent buildings are at the forefront due to its main objective of providing comfort to users and saving energy through intelligent control systems. Intelligent systems have been reported to offer comfort to a single user or averaging the comfort of multiple users without considering that their needs may be different from those of other users. This work defines a versatile model for a multi-user intelligent system that negotiates with the resources of the environment to offer visual comfort to multiple users with different profiles, activities and priorities using soft-computing algorithms. In addition, this model makes use of external lighting to provide the recommended amount of illumination for each user without having to totally depend on artificial lighting, inducing there will be an energy efficiency but without measuring it.