Search results for "Genetic algorithm"
showing 10 items of 834 documents
A Memetic Island Model for Discrete Tomography Reconstruction
2011
Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawba…
Soft computing-based aggregation methods for human resource management
2008
Abstract We are interested in the personnel selection problem. We have developed a flexible decision support system to help managers in their decision-making functions. This DSS simulates experts’ evaluations using ordered weighted average (OWA) aggregation operators, which assign different weights to different selection criteria. Moreover, we show an aggregation model based on efficiency analysis to put the candidates into an order.
Soft Computing Techniques for Portfolio Selection: Combining SRI with Mean-Variance Goals
2014
A fuzzy portfolio selection model is presented incorporating a socially responsible goal without discarding a priori financially good portfolios or weakening a priori the financial goals. Hence, the optimal portfolios it provides could be either efficient from the strictly financial point of view or non-efficient if leaving the efficient frontier substantially improves the degree of social responsibility. The model can be used to direct heuristic procedures in order to select a reduced number of various alternatives from which the investor can directly make a final decision.
JEM–X science analysis software
2003
The science analysis of the data from JEM-X on INTEGRAL is performed through a number of levels including corrections, good time selection, imaging and source finding, spectrum and light-curve extraction. These levels consist of individual executables and the running of the complete analysis is controlled by a script where parameters for detailed settings are introduced. The end products are FITS files with a format compatible with standard analysis packages such as XSPEC. Martinez Nuñez, Silvia, Silvia.Martinez@uv.es
Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database
2012
International audience; Large soil spectral libraries compiling thousands of NIR (Near Infrared) reflectance spectra have been created encompassing a wide diversity and heterogeneity of spectra. Among the many chemometric approaches to the calibration of chemical and physical properties from these large libraries, local calibrations have the advantage of being able to select the most similar spectra to the spectrum of a target sample. This is particularly relevant when dealing with highly heterogeneous media such as soils, where the mineral matrix has a strong influence on spectral features. A crucial step in the implementation of local calibration procedures is the construction of local ne…
Genetic selection for reduced Somatic Cell Counts in sheep milk: A review
2015
Mastitis is an inflammation of the udder, mainly caused by bacteria, and leads to economic loss, due to discarded milk, reduced milk production, reduced milk quality and increased health costs in both dairy sheep and cattle. Selecting for increased genetic resistance to mastitis can be done directly or indirectly, with the indirect selection corresponding to a prediction of the bacteriological status of the udder based on traits related to the infection. The most frequently used indirect method is currently milk somatic cell count (SCC) or somatic cell score (SCS). This review reports the state of the art relating to the genetic basis of mastitis resistance in sheep and explores the opportu…
Genetic parameters for somatic cell score according to udder infection status in Valle del Belice dairy sheep and impact of imperfect diagnosis of in…
2010
Abstract Background Somatic cell score (SCS) has been promoted as a selection criterion to improve mastitis resistance. However, SCS from healthy and infected animals may be considered as separate traits. Moreover, imperfect sensitivity and specificity could influence animals' classification and impact on estimated variance components. This study was aimed at: (1) estimating the heritability of bacteria negative SCS, bacteria positive SCS, and infection status, (2) estimating phenotypic and genetic correlations between bacteria negative and bacteria positive SCS, and the genetic correlation between bacteria negative SCS and infection status, and (3) evaluating the impact of imperfect diagno…
The Multivariate Individual Selection of Diagnostic Tests and the Reserved Diagnostic Statement: An Optimum Combination of Two New Methods for the Co…
1984
A combination of two new methods for the diagnostic procedure in computer-aided differential diagnosis is presented. It is constructed on the basis of new results of our own in the field of mathematical decision theory and is demonstrated by the differential diagnosis of congenital heart diseases by means of ECG features.
A penalized approach to covariate selection through quantile regression coefficient models
2019
The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…
Calibrating a microscopic traffic simulation model for roundabouts using genetic algorithms
2018
The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB®. Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for ca…