881 resultados para Pare to archived genetic algorithm


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Three-Phase Induction Motors (TIM) and Arc Welding Machines (AWM) are loads of special behavior widely used in industrial and commercial installations, and therefore may contribute significantly to the deterioration of the quality of energy supplied by utilities. This paper proposes a modeling in constant power of the unbalanced TIM starting using Genetic Algorithm (GA) and AWM short-circuit based on their statics characteristics curves. The proposed models are compared with the conventional models in the literature. The results showed the good performance of the proposed models, allowing a more precise analysis of the real requests of these loads.

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P>In this study, Bayesian analysis under a threshold animal model was used to estimate genetic correlations between morphological traits (body structure, finishing precocity and muscling) in Nelore cattle evaluated at weaning and yearling. Visual scores obtained from 7651 Nelore cattle at weaning and from 4155 animals at yearling, belonging to the Brazilian Nelore Program, were used. Genetic parameters for the morphological traits were estimated by two-trait Bayesian analysis under a threshold animal model. The genetic correlations between the morphological traits evaluated at two ages of the animal (weaning and yearling) were positive and high for body structure (0.91), finishing precocity (0.96) and muscling (0.94). These results indicate that the traits are mainly determined by the same set of genes of additive action and that direct selection at weaning will also result in genetic progress for the same traits at yearling. Thus, selection of the best genotypes during only one phase of life of the animal is suggested. However, genetic differences between morphological traits were better detected during the growth phase to yearling. Direct selection for body structure, finishing precocity and muscling at only one age, preferentially at yearling, is recommended as genetic differences between traits can be detected at this age.

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Genetic gains predicted for selection, based on both individual performance and progeny testing, were compared to provide information to be used in implementation of progeny testing for a Nelore cattle breeding program. The prediction of genetic gain based on progeny testing was obtained from a formula, derived from methodology of Young and Weiler (J. Genetics 57: 329-338, 1960) for two-stage selection, which allows prediction of genetic gain per generation when the individuals under test have been pre-selected on the basis of their own performance. The application of this formula also allowed determination of the number of progeny per tested bull needed to maximize genetic gain, when the total number of tested progeny is limited.

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This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

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Heterogeneity of variances for milk yield (MY) was determined for Brazilian and Colombian herds. The herds were grouped as high or low variability within each country, using as criterion the phenotypic standard deviation (PSD) of MY in the contemporary groups of cows, from the first to the sixth calving. Brazilian and Colombian herds with PSD greater than 1,168 kg or 1,012 kg, respectively, were classified as high variability while the herd groups with values lower than those were classified as low variability. The genetic parameters for MY within each herd group were estimated using bivariate analysis in an animal model and the restricted maximum likelihood method with a derivative free algorithm, using 72,280 first lactations of cows, daughters of 1,880 sires. Heterogeneous variances were found, and Brazilian herds with high PSD had the greatest additive and residual genetic variances and heritability coefficients for MY. MY genetic correlation coefficients between herds of high and low variability within each country were 0.96 and 0.93 while between countries they varied from 0.72 to 0.81, suggesting that there was a reclassification of animals in the two countries and also heterogeneity of variances. This phenomenon leads to the questioning of the strategy of imported semen usage and the need to do genetic evaluations to identify sires with greater genetic potential for (sub) tropical environmental conditions.

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Reliability of power supply is related, among other factors, to the control and protection devices allocation in feeders of distribution systems. In this way, optimized allocation of sectionalizing switches and protection devices in strategic points of distribution circuits, improves the quality of power supply and the system reliability indices. In this work, it is presented a mixed integer non-linear programming (MINLP) model, with real and binary variables, for the sectionalizing switches and protection devices allocation problem, in strategic sectors, aimed at improving reliability indices, increasing the utilities billing and fulfilling exigencies of regulatory agencies for the power supply. Optimized allocation of protection devices and switches for restoration, allows that those faulted sectors of the system can be isolated and repaired, re-managing loads of the analyzed feeder into the set of neighbor feeders. Proposed solution technique is a Genetic Algorithm (GA) developed exploiting the physical characteristics of the problem. Results obtained through simulations for a real-life circuit, are presented. © 2004 IEEE.

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This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n-1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

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Objective: Taking into consideration that DNA damage plays an important role in carcinogenesis, the purpose of this study was to evaluate whether regular and white mineral trioxide aggregate (MTA) are able to induce genetic damage in primary human cells. Study design: Human peripheral lymphocytes obtained from 10 healthy volunteers were exposed to 2 presentation forms of MTA at final concentrations ranging from 1 to 1000 μg/mL for 1 hour at 37°C. The negative control group was treated with vehicle control (phosphate buffer solution, PBS) for 1 hour at 37°C and the positive control group was treated with hydrogen peroxide (at 100 μM) for 5 minutes on ice. Results were analyzed by the Friedman nonparametric test. Results: The results pointed out that either regular or white MTA in all concentrations tested did not induce DNA breakage in human peripheral lymphocytes as depicted by the mean tail moment. Conclusion: In summary, our results indicate that exposure to MTA may not be a factor that increases the level of DNA lesions in human peripheral lymphocytes as detected by single cell gel (comet) assay. © 2006 Mosby, Inc. All rights reserved.

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In this paper, an expert and interactive system for developing protection system for overhead and radial distribution feeders is proposed. In this system the protective devices can be allocated through heuristic and an optimized way. In the latter one, the placement problem is modeled as a mixed integer non-linear programming, which is solved by genetic algorithm (GA). Using information stored in a database as well as a knowledge base, the computational system is able to obtain excellent conditions of selectivity and coordination for improving the feeder reliability indices. Tests for assessment of the algorithm efficiency were carried out using a real-life 660-nodes feeder. © 2006 IEEE.

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In DNA microarray experiments, the gene fragments that are spotted on the slides are usually obtained by the synthesis of specific oligonucleotides that are able to amplify genes through PCR. Shotgun library sequences are an alternative to synthesis of primers for the study of each gene in the genome. The possibility of putting thousands of gene sequences into a single slide allows the use of shotgun clones in order to proceed with microarray analysis without a completely sequenced genome. We developed an OC Identifier tool (optimal clone identifier for genomic shotgun libraries) for the identification of unique genes in shotgun libraries based on a partially sequenced genome; this allows simultaneous use of clones in projects such as transcriptome and phylogeny studies, using comparative genomic hybridization and genome assembly. The OC Identifier tool allows comparative genome analysis, biological databases, query language in relational databases, and provides bioinformatics tools to identify clones that contain unique genes as alternatives to primer synthesis. The OC Identifier allows analysis of clones during the sequencing phase, making it possible to select genes of interest for construction of a DNA microarray. ©FUNPEC-RP.

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This chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach. © 2008 Springer-Verlag Berlin Heidelberg.

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This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering uncertainty in demand and generation. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The model presented results in an optimization problem that is solved using a specialized genetic algorithm. The results obtained for known systems from the literature show that cheaper plans can be found satisfying the uncertainty in demand and generation. ©2008 IEEE.

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This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering open access. The methodology finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with multiples generation scenarios. The model presented is solved using a specialized genetic algorithm. The methodology is tested in a system from the literature. ©2008 IEEE.

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This paper presents a methodology and a mathematical model to solve the expansion planning problem that takes into account the effect of contingencies in the planning stage, and considers the demand as a stochastic variable within a specified range. In this way, it is possible to find a solution that minimizes the investment costs guarantying reliability and minimizing future load shedding. The mathematical model of the expansion planning can be represented by a mixed integer nonlinear programming problem. To solve this problem a specialized Genetic Algorithm combined with Linear Programming was implemented.