Fuentes de la inflación en México, 1989-2000: Un análisis multicausal de corrección de errores

This article analyzes the determinants of inflation in Mexico during the 1989–2000 period. Inflation is modelled as a function of deviations in the long-run relations that may exist in the monetary, labour and exchange rate markets. By using cointegration techniques, we obtain an error-correction model where money excess, wage pressure and deviations of the Purchasing Power Parity are possible sources of inflation. The model includes an inertial factor and a policy component due to government-controlled price changes in certain goods. The results show that all the factors mentioned have contributed to the determination of the inflationary dynamics in Mexico.

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic

Aplicación del estudio del trabajo para mejorar la productividad en el montaje de plantas dosificadoras de concreto en Unicon, 2017

La investigación abordó la importancia de la aplicación del Estudio del Trabajo para mejorar la Productividad en el montaje de plantas dosificadoras de concreto en Unicon, 2017. Por ello su objetivo fue aplicar este método en la empresa, así elevar su productividad. El tipo de estudio correspondió al aplicativo de diseño, cuasi experimental dentro del enfoque cuantitativo. La población lo conformaron los días que se utiliza en un montaje de planta dosificadora de concreto, siendo N= 24, además su muestra fue N=n. La técnica para recolectar los datos fue la observación de datos con su instrumento ficha de recolección de datos validados por juicio de expertos, siendo estos procesados con el programa estadístico SPSS V.24. Finalmente se concluyó que la aplicación del estudio de trabajo incremento la productividad en los montajes de plantas dosificadoras de concreto en Unicon, 2016. En conclusión la aplicación del estudio del trabajo a través de la reducción de las actividades y tiempos improductivos en los montajes de plantas dosificadoras mejorara la productividad en un 17%

Factores de riesgo para el desarrollo de bronquiolitis severa en niños menores de 2 años admitidos al Hospital del Niño, Panamá de diciembre de 2013 a abril 2014.

La bronquiolitis es una de las enfermedades respiratorias más frecuentes de la infancia y los cuadros severos aumentan la carga sobre los servicios de salud. Se realizó un estudio caso control para identificar factores asociados con severidad de la bronquiolitis y paralelamente un estudio epidemiológico para identificar estacionalidad de los virus circulantes entre mayo 2013 a abril 2014. Se encontró un patrón bifásico entre julio y enero para virus Sincitial Respiratorio, monofásico entre diciembre-abril para Rinovirus y Parainfluenza 3 entre marzo y mayo. Los factores de riesgo identificados para bronquiolitis severa fueron bajo peso /OR 5.58 IC95% 2.47–12.57), prematuridad <32 semanas, (=R 13.29 IC95% 1.777-324.6), presencia de cualquier comorbilidad (OR 3.42 IC95% 1.6-7.3), la convivencia con niños < de 5 años en el hogar (OR 3.0 IC95% 1.4-6.4) y la historia de apnea (OR 17.2 IC95% 2.06-143.72)

Multiple crossovers on multiple parents for the multiobjective flow shop problem

The Flow Shop Scheduling Problem have been tackled using different techniques which goes from mathematical techniques like Branch and Bound to metaheuristics like evolutionary algorithms (EAs). Although in the real world this problem will be found more frequently with more than one objective, most work been done is based on a single objective. Evolutionary algorithms are very promising in this area because the outcome of a multiobjective problem is a set of optimal solutions (the Pareto Front) which EAs can provide in a single run. Yet another advantage of EA’s over other techniques is that they are less liable to the shape or continuity of the Pareto Front. In this work, we show three implementations of multiobjective Evolutionary Algorithms. The first one uses Single Crossover Per Couple (SCPC), while the other two use Multiple Crossover on Multiple Parents (MCMP), continuing with previous works[7, 8]. These two methods show an enhancement on the performance of the first method. Details of implementation and results are discussed.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Multiplicity of parents and crossovers, the stud and the neh heuristic for searching the optimal makespan in fssp

Different evolutionary approaches using genetic algorithms were proposed to solve the Flow Shop Scheduling Problem (FSSP). Variants point to the selection mechanism, genetic operators and the decision to include or not in the initial population an individual generated by some conventional heuristic (Reeves). New trends to enhance evolutionary algorithms for solving the FSSP introduced multiple-crossovers-per couple (MCPC) and multiple-crossovers-on-multiple-parents (MCMP). MCMP-S, a multiple-crossovers-on-multiple-parents variant, selects the stud (breeding individual) among the multiple intervening parents and mates it, more than once, with every other parent in a multiple crossover operation. In previous works, two versions of MCMP-S were faced. In the first one (MCMP-SOP), the stud and every other parent were selected from the old population. In the second one (MCMP-SRI), the stud was selected from the old population, and the other parents (random immigrants) were generated randomly. This paper introduces MCMP-NEH. The idea is to use the NEH heuristic, where the stud mates individuals in the mating pool coming from two sources: random immigrants and NEH-based individuals. These NEH-individuals are produced from randomly chosen individuals of the population and used as the starting points of the NEH heuristic. Experiments were conducted to contrast this novel proposal with a conventional evolutionary algorithm, with the only objective of establishing the improvement degree despite computational effort. Implementation details and a comparison of results for a set of flow shop scheduling instances of distinct complexity, using every evolutionary approach, are shown.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

MCPC: another approach to crossover in genetic algorithms

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where . no algorithm is known. The main operator, which is the driving force of genetic algorithms, IS crossover. It combines the features of two parents and produces two offspring. This paper propases a Multiple Crossover Per Couple (MCPC) approach as an altemate method for crossover operators.Eje: Diseño de algoritmosRed de Universidades con Carreras en Informática (RedUNCI

An efficient adaptative predictive load balancing method for distributed systems

When allocating processors to processes in a distributed system, load balancing is a main concern of designers. By its implementation, system performance can be enhanced by equally distributing the dynamically changing workload and consequently user expectation are improved through an additional reduction on mean response time. In this way, through process migration, a rational and equitable use of the system computational power is achieved, preventing degradation of system performance due to unbalanced work of processors. This article presents an Adaptative Predictive Load Balancing Strategy (APLBS), a variation of Predictive Load Balancing Strategy (PLBS) reported elsewhere [1]. As PLBS, APLBS is a sender initiated, prediction-based strategy for load balancing. The predictive approach is based on estimates given by a weighted exponential average [12] of the load condition of each node in the system. The new approach tries to minimise traffic en the network selecting the most suitable subset of candidates to request migration and the novel aspect is that the size of this subset is adaptative with respect to the system workload. APLBS was contrasted against Random (R), PLBS and Flexible Load Sharing (FLS) [7] strategies on diverse scenarios where the load can be characterised as static or dynamic. A comparative analysis of mean response time, acceptance hit ratio and number of migration failures under each strategy is reported.Sistemas Distribuidos - Redes ConcurrenciaRed de Universidades con Carreras en Informática (RedUNCI