Genetic Algorithms
Mostrando 25-36 de 475 artigos, teses e dissertações.
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25. Viscoelastic Relaxation Modulus Characterization Using Prony Series
AbstractThe mechanical behavior of viscoelastic materials is influenced, among other factors, by parameters like time and temperature. The present paper proposes a methodology for a thermorheologically and piezorheologically simple characterization of viscoelastic materials in the time domain based on experimental data using Prony Series and a mixed optimiza
Lat. Am. j. solids struct.. Publicado em: 2015-04
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26. QSAR Study of the Inhibitors of the Acetyl-CoA Carboxylase 1 and 2 using Bayesian Regularized Genetic Neural Networks: A Comparative Study
Linear and non-linear quantitative structure-activity relationship (QSAR) models were presented for modeling and predicting anti-diabetic activities of a set of inhibitors of acetyl-CoA carboxylase 1 and 2 (ACC1 and ACC2). Different algorithms were utilized to choose the best variables among large numbers of descriptors and then these selected descriptors we
J. Braz. Chem. Soc.. Publicado em: 2015-03
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27. On Using Quasi-Newton Algorithms of the Broyden Class for Model-to-Test Correlation
ABSTRACT: The correlation of a model with test results is a common task in engineering. Often genetic algorithms or adaptive particle swarm algorithms are used for this task. In this paper another approach is presented using two quasi-Newton algorithms of the class defined by Broyden. A study was conducted with thermal models showing the performance of this
J. Aerosp. Technol. Manag.. Publicado em: 2014-12
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28. Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network
Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional
Braz. arch. biol. technol.. Publicado em: 2014-12
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29. AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS
Random key genetic algorithms are heuristic methods for solving combinatorial optimization problems. They represent solutions as vectors of randomly generated real numbers, the so-called random keys. A deterministic algorithm, called a decoder, takes as input a vector of random keys and associates with it a feasible solution of the combinatorial optimization
Pesqui. Oper.. Publicado em: 2014-08
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30. A GENETIC ALGORITHM FOR THE ONE-DIMENSIONAL CUTTING STOCK PROBLEM WITH SETUPS
This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the concepts of genetic algorithms is proposed to solve the problem. This heuristic is empirically analyzed by solving
Pesqui. Oper.. Publicado em: 2014-08
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31. Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN
The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This
Braz. arch. biol. technol.. Publicado em: 2014-02
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32. Cash management policies by evolutionary models: a comparison using the Miller-Orr model
This work aims to apply genetic algorithms (GA) and particle swarm optimization (PSO) to managing cash balance, comparing performance results between computational models and the Miller-Orr model. Thus, the paper proposes the application of computational evolutionary models to minimize the total cost of cash balance maintenance, obtaining the parameters for
JISTEM J.Inf.Syst. Technol. Manag.. Publicado em: 2013-12
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33. An ant colony algorithm applied to lay-up optimization of laminated composite plates
Ant colony optimization (ACO) is a class of heuristic algorithms proposed to solve optimization problems. The idea was inspired by the behavior of real ants, related to their ability to find the shortest path between the nest and the food source. ACO has been applied successfully to different kinds of problems. So, this manuscript describes the development a
Lat. Am. j. solids struct.. Publicado em: 2013-05
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34. Pró-dieta: gerador automático de cardápios personalizados baseado em algoritmos genéticos
O uso de sistemas computacionais e técnicas de Inteligência Artificial (IA) para resolver problemas do mundo real está cada vez mais frequente e, na área nutricional, não é diferente. No Brasil e no mundo, os sistemas mais encontrados são os de auxílio à prescrição de cardápios e formulação de dietas. Este trabalho apresenta uma metodologia de
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 05/10/2012
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35. Função de avaliação dinâmica em algoritmos genéticos aplicados na predição de estruturas tridimensionais de proteínas / Genetic Algorithms with Dynamic Fitness Functions Applied to Tridimensional Protein Structure Prediction
The protein structure prediction can be seen as an optimization problem where given an amino acid sequence, the tertiary protein structure must be found amongst many possible by obtaining energy functions minima. Many researchers have been proposing Evolutionary Computation strategies to find tridimensional structures of proteins; however results are not alw
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 28/09/2012
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36. Algoritmos para o custo médio a longo prazo de sistemas com saltos markovianos parcialmente observados / Algorithms for the long run average cost for linear systems with partially observed Markov jump parameters
In this work we are interested in the optimal control for the long run average cost (LRAC) problem for linear systems with Markov jump parameters (LSMJP), using heuristic methods like first generation evolutionary algorithms - genetic algorithm (GA) - and second generation algorithms including UMDA (Univariate Marginal Distribution Algorithm) and BOA (Bayesi
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 13/08/2012