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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 contribute to this line of research, so this study uses a dataset with information from sexually active women between 18 and 50 years old, including 17 numerical attributes of microorganisms and bacteria with positive and negative results for BV. These values were semantically categorized for the Apriori algorithm to create the association rules, using support, confidence, and lift as statistical metrics to evaluate the quality of the rules, and incorporate those results in the objective function of the DE algorithm. To guide the evolutionary process we also incorporated the knowledge of a human expert represented as a set of biologically meaningful constraints. Thus, we were able to compare the performance of the rand/1/bin and best/1/bin versions from Differential Evolution to analyze the results of 30 independent executions. Therefore the experimental results allowed a reduced subset of biologically meaningful association rules by their executions, dimension, and DE version to be selected.

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

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 are well-known problems in them. Other approaches, such as incremental induction, classifier ensembles, and the global search in the decision-tree-space, have been implemented to overcome these problems. In particular, metaheuristics such as simulated annealing, genetic algorithms, genetic programming, and ant colony optimization have been used to induce compact and accurate decision trees. This paper presents a state-of-the-art review of the use of single-solution-based metaheuristics and swarm and evolutionary computation algorithms to build decision trees as classification models. We outline the decision-tree-induction process components and detail the existing literature studies on metaheuristic-based approaches to building these classifiers. Several timelines showing the chronological order in which these approaches were introduced in the literature are included. A summary analysis of these studies is also conducted, focusing on their internal components and experimental studies. This work provides a useful reference point for future research in this field.

This paper describes one grid-based genetic algorithm approach to solve the vehicle routing problem with time windows in one experimental cluster MiniGrid. Clusters used in this approach are located in two Mexican cities (Cuernavaca and Jiutepec, Morelos) securely communicating with each other since they are configured as one virtual private network, and its use as a single set of processors instead of isolated groups allows one to increase the computing power to solve complex tasks. The genetic algorithm splits the population of candidate solutions in several segments, which are simultaneously mutated in each process generated by the MiniGrid. These mutated segments are used to build a new population combining the results produced by each process. In this paper, the MiniGrid configuration scheme is described, and both the communication latency and the speedup behavior are discussed. Experimental results show one information exchange reduction through the MiniGrid clusters as well as an improved behavior of the evolutionary algorithm. A statistical analysis of these results suggests that our approach is better as a combinatorial optimization procedure as compared with other methods.

Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a simulated-annealing-based (SA) approach applying a variable neighborhood strategy in a continuous space to generate competitive and different alternative paths to solve the corridor planning problem. The variable neighborhood method randomly selects two points from a variable interval of the current solution generated by SA creating pseudo-random paths inside a corridor and finding spatially different alternatives. The proposed approach is evaluated with three practical problems using real topographic data from the Veracruz Basin in Mexico. The experimental results show that this approach obtains efficient and competitive solutions with improvements above 18% over those gotten by the compared method.

This research proposes a genetic algorithm that provides a solution to the problem of deficient distribution of drinking water via the current hydraulic network in the neighborhood “Fraccionamiento Real Montecasino” (FRM), in Huitzilac, Morelos, Mexico. The proposed solution is the addition of new elements to the FRM network. The new elements include storage tanks, pipes, and pressure-reducing valves. To evaluate the constraint satisfaction model of mass and energy conservation, the hydraulic EPANET solver (HES) is used with an optimization model to minimize the total cost of changes in the network (new pipes, tanks, and valves). A genetic algorithm was used to evaluate the optimization model. The analysis of the results obtained by the genetic algorithm for the FRM network shows that adequate and balanced pressures were obtained by means of small modifications to the existing network, which entailed minimal costs. Simulations were performed for an extended period, which means that the pressure was obtained by simulation with HSE at one-hour intervals, during the algorithm execution, to verify adequate pressure at a specific point in the system, or to make corrections to ensure proper distribution, this with the aim of having a final optimized network design.

En este artículo se describe una Metodología Ágil de Desarrollo de Software Incremental e Iterativa para la migración de Sistemas Heredados (MADIISH) que permitirá organizar el proceso de migración por fases desde un análisis modular del sistema heredado a migrar, el establecimiento de procesos de negocio e implementación de modelos de datos, así como la definición de etapas de pruebas piloto y la posibilidad de generar módulos que permitan mantener la interoperabilidad entre los nuevos sistemas desarrollados y los sistemas activos. MADIISH se aplica para la migración de sistemas obsoletos que pueden ser de misión crítica, alta disponibilidad, de gran flujo de datos o para sistemas orientados a la planificación de recursos empresariales (Enterprise Resource Planning, ERP).

This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy. Considering that: (1) the task of finding an optimal hyperplane with real-valued coefficients is a complex optimization problem in a continuous space, and (2) metaheuristics have been successfully applied for solving this type of problems, in this work a differential evolution algorithm is applied with the aim of finding near-optimal hyperplanes that will be used as test conditions of an oblique decision tree. The experimental results show that this approach induces more accurate classifiers than those produced by other proposed induction methods.

En este artículo se presenta la aplicación de un algoritmo genético cooperativo para el cálculo de trayectorias que los dedos de una mano robótica configurable deben seguir para sujetar un objeto en dos dimensiones. A partir de un objetivo común, se establece el conjunto de movimientos coordinados para alcanzar la meta. Se describen las características de la sujeción de objetos, que involucra tanto la determinación de los puntos de contacto como la definición de las trayectorias de los dedos para alcanzar dichos puntos. Además de los algoritmos para la sujeción y para las trayectorias, se muestran los resultados experimentales.

El comercio electrónico es un campo en constante evolución que involucra un conjunto complejo de redes, bases de datos, tecnologías de información y procesos. Con la presencia de internet, las empresas requieren aplicaciones que brinden seguridad, administración confiable, integración, automatización y estandarización de procesos e información, además de mecanismos para apoyar la toma de decisiones y novedosas formas de presentar sus productos o servicios. Actualmente existen tecnologías, como los servicios web, los agentes y el modelado virtual, que soportan las necesidades requeridas, sin embargo trabajan de manera aislada. Con la finalidad de lograr procesos exitosos, automatizados, con manejo eficiente de información y que conjunten las ventajas de cada tecnología, se propone un modelo de integración lógica de las mismas, mediante el cual se brinde a las empresas una solución integral para sus diferentes necesidades: operacionales, de administración eficiente de la información y de presentación novedosa de ésta en modelos de negocio B2B.

En este artículo se muestran los resultados obtenidos de la paralelización por datos del codificador H.264/AVC versión 16.0 para video de alta definición. Con esta paralelización se logra codificación en tiempo real redimensionando una resolución de entrada de 640x400 pixeles en tres resoluciones de salida (640x400, 720x480 y 1280x720 pixeles), disminuyendo así el espacio de almacenamiento con respecto al uso de una resolución de entrada por cada resolución de salida; además durante las pruebas realizadas se comprobó que al utilizar el redimensionamiento se acelera ligeramente la codificación disminuyendo así la cantidad de procesadores necesarios para la codificación en tiempo real.

Este artículo muestra una descripción de dos algoritmos paralelos para la codificación de video con balanceo predictivo de carga: Uno basado en la estimación de movimiento exhaustivo y el otro adaptivo. Ambos se basan en el estándar H264 que incluye una paralelización a nivel de GOPs, haciendo una distribución de los mismos en un cluster por medio del protocolo estándar de paso de mensajes MPI. La manera en que se distribuye la carga es mediante la combinación de dos esquemas: inicialmente por preasignación y posteriormente predicción. La idea es que una vez hecha la repartición inicial (preasignación), se procede a determinar la complejidad de los GOPs en tiempo de codificación (predicción), para posteriormente hacer una planificación que permita balancear la codificación, enviando los GOPs más pesados a los procesadores que codificaron previamente GOPs ligeros y viceversa. Como el adaptivo es una versión mejorada del exhaustivo, la evaluación esta en base dicha mejora fin de determinar el promedio del aprovechamiento a partir de resultados obtenidos con diferentes números de nodos de procesamiento, diversos tipos y resoluciones de videos de tal forma que nos permita establecer una ecuación con la cual se puede calcular el número de nodos codificadores necesarios para obtener tiempo real.

En este artículo se plantea una propuesta de modelado orientado a objetos del problema de balanceo de ecuaciones químicas. Este modelo se utiliza para desarrollar un conjunto de clases que aplican métodos algebraicos basados en manejo de matrices para encontrar los coeficientes estequiométricos que balancean una ecuación química. También se define una gramática utilizada en la validación de la ecuación y se describe la aplicación de tres algoritmos algebraicos para resolver un conjunto de ecuaciones químicas. Adicionalmente, se describe el diseño de una página web que pretende ser un banco de prueba para este tipo de problemas. En este artículo se presenta el modelo de clases, así como los resultados experimentales de la aplicación de los algoritmos algebraicos.

Around the world there have recently been new and more powerful computing platforms created that can be used to work with computer science problems. Some of these problems that are dealt with are real problems of the industry; most are classified by complexity theory as hard problems. One such problem is the vehicle routing problem with time windows (VRPTW). The computational Grid is a platform which has recently ventured into the treatment of hard problems to find the best solution for these. This chapter presents a genetic algorithm for the vehicle routing problem with time windows. The algorithm iteratively applies a mutation operator, first of the intelligent type and second of the restricting type. The algorithm takes advantage of Grid computing to increase the exploration and exploitation of the solution space of the problem. The Grid performance is analyzed for a genetic algorithm and a measurement of the latencies that affect the algorithm is studied. The convenience of applying this new computing platform to the execution of algorithms specially designed for Grid computing is presented.

In this work is defined a scheme of two stages for sending population segments of one Parallel Genetic Algorithm (PGA) to the nodes of an experimental grid called “Tarantula miniGrid”. The technique used to link the clusters and to configure the Tarantula mini Grid is described and the effects of latency in the communications between these clusters and the behavior of the speedup are discussed. The PGA is used for solving instances of the Vehicle Routing Problem with Time Windows (VRPTW). Results of this approach show that this communication scheme increases the efficiency of PGA in a grid environment and produces greater exploration and exploitation of the solution space of VRPTW.