Variantes del algoritmo CHC para proyectar redes de radio frecuencia en comunicaciones inalámbricas

En el presente trabajo se aplica una serie de versiones del algoritmo genético no convencional denominado Cross generational elitist selection Heterogeneous recombination Cataclysmic mutation algorithm (CHC ) para resolver el problema de diseño de red de radio (RND). Se utiliza un conjunto de algoritmos genéticos para realizar una comparativa de rendimiento de los algoritmos propuestos. Se emplea una función objetivo basada en la eficiencia de iluminación de la señal. Se utiliza la variabilidad genética de la población como parámetro de convergencia y detección de incesto y se propone el uso de la variabilidad del mejor individuo como mecanismo de sacudida. Esto permite generar poblaciones dinámicas conforme a las soluciones más promisorias generando diferentes espacios de búsqueda. Los resultados obtenidos por los algoritmos propuestos son satisfactorios.

An genetic algorithm approach for profiling computational performance measures

This paper present a Genetic Algorithm(GA) approach for clustering data metric of computational performance measures collected from vmstat and sar tools. The proposed work models the genes, chromosomes, species and environment based on the dataset and presents an algorithm to analyze patterns and classify the records. The proposed method submits the performance information to an N-Dimensional Histogram in order to obtain the distribution of data that is used as input to the cluster initialization. The individual from each species undergoes successive crossover, mutation and selection operations to improve and evolve the initial population to a given environment state. The fitness-function is determined by the N-Dimensional Euclidean distance. The selection method is based on the Roulette-Wheel Selection, Elitist Selection and Truncation Selection. The results presented were obtained from seven test scenarios.

An genetic algorithm approach for profiling computational performance measures

This paper present a Genetic Algorithm(GA) approach for clustering data metric of computational performance measures collected from vmstat and sar tools. The proposed work models the genes, chromosomes, species and environment based on the dataset and presents an algorithm to analyze patterns and classify the records. The proposed method submits the performance information to an N-Dimensional Histogram in order to obtain the distribution of data that is used as input to the cluster initialization. The individual from each species undergoes successive crossover, mutation and selection operations to improve and evolve the initial population to a given environment state. The fitness-function is determined by the N-Dimensional Euclidean distance. The selection method is based on the Roulette-Wheel Selection, Elitist Selection and Truncation Selection. The results presented were obtained from seven test scenarios.

A Computational Grid Scheduling Model to Maximize Reliability Using Modified GA

This paper presents a grid scheduling model to schedule a job on the grid with the objective of ensuring maximum reliability to the job under the current grid state. The model schedules a modular job to those resources that suit the job requirements in terms of resources while offering the most reliable environment. The reliability estimates depict true grid picture and considers the contribution of the computational resources, network links and the application awaiting allocation. The scheduling executes the interactive jobs while considering the looping structure. As scheduling on the grid is an NP hard problem, soft computing tools are often applied. This paper applies Modified Genetic Algorithm (MGA), which is an elitist selection method based on the two threshold values, to improve the solution. The MGA works on the basis of partitioning the current population in three categories: the fittest chromosomes, average fit chromosomes and the ones with worst fitness. The worst are dropped, while the fittest chromosomes of the current generation are mated with the average fit chromosomes of the previous generation to produce off-spring. The simulation results are compared with other similar grid scheduling models to study the performance of the proposed model under various grid conditions.

A Computational Grid Scheduling Model To Maximize Reliability Using Modified GA

This paper presents a grid scheduling model to schedule a job on the grid with the objective of ensuring maximum reliability to the job under the current grid state. The model schedules a modular job to those resources that suit the job requirements in terms of resources while offering the most reliable environment. The reliability estimates depict true grid picture and considers the contribution of the computational resources, network links and the application awaiting allocation. The scheduling executes the interactive jobs while considering the looping structure. As scheduling on the grid is an NP hard problem, soft computing tools are often applied. This paper applies Modified Genetic Algorithm (MGA), which is an elitist selection method based on the two threshold values, to improve the solution. The MGA works on the basis of partitioning the current population in three categories: the fittest chromosomes, average fit chromosomes and the ones with worst fitness. The worst are dropped, while the fittest chromosomes of the current generation are mated with the average fit chromosomes of the previous generation to produce off-spring. The simulation results are compared with other similar grid scheduling models to study the performance of the proposed model under various grid conditions.