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Global optimization of energy and production in process industries: a genetic algorithm application

Santos, Amâncio; Dourado, António
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Artigo de Revista Científica Formato: aplication/PDF
Português
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The process industries exhibit an increasing need for efficient management of all the factors that can reduce their operating costs, leading to the necessity for a global multi-objective optimization methodology that will enable the generation of optimum strategies, fulfilling the required restrictions. In this paper, a genetic algorithm is developed and applied for the optimal assignment of all the production sections in a particular mill in the kraft pulp and paper industry, in order to optimize energy the costs and production rate changes. This system is intended to implement all programmed or forced maintenance shutdowns, as well as all the reductions imposed in production rates.; http://www.sciencedirect.com/science/article/B6V2H-3WJFJY7-D/1/7465ac585e8d9ac2ef4ea20cafe98712

Use of the q-Gaussian mutation in evolutionary algorithms

TINOS, Renato; YANG, Shengxiang
Fonte: SPRINGER Publicador: SPRINGER
Tipo: Artigo de Revista Científica
Português
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This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.; FAPESP; CNPq in Brazil; Engineering and Physical Sciences Research Council (EPSRC) of the UK[EP/E060722/1]; Engineering and Physical Sciences Research Council (EPSRC) of the UK[EP/E060722/2]

Projeto de formação de células de manufatura através da utilização de algoritmos genéticos

Izquierdo, Rafael Crespo
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
Português
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Nos modernos sistemas produtivos exige-se crescente aumento na eficiência global, o que implica em otimização nas etapas de processo. Neste contexto, o arranjo de um layout industrial é um conceito fundamental para a eficácia de um sistema, que deve ser considerada na sua concepção. Em um ambiente industrial podem-se conceber diversos tipos de layout, de acordo com a diversidade de itens e do modo de produção adotado, da demanda de mercado. O presente trabalho aborda o projeto de layout do tipo celular, uma modalidade de arranjo utilizado na engenharia industrial que permite atender diversidade de produção compatível com certa flexibilidade operacional. Dentre as diversas técnicas e abordagens aplicadas para a formação de células de manufatura, adota-se especificamente uma aplicação de Algoritmo Genético, implementada através de uma interface em Matlab, onde são abordados tópicos como: a geração da população inicial, a codificação do cromossomo,a função objetivo, as restrições do problema, os operadores de cruzamento e mutação e a confiabilidade do algoritmo proposto. Dos resultados obtidos, constata-se que os Algoritmos Genéticos são ferramentas confiáveis para a otimização de sistemas de fabricação...

Propostas de metodologias para identificação e controle inteligentes; Proposals of methodologies for intelligent identification and control

Ginalber Luiz de Oliveira Serra
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 16/09/2005 Português
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Esta tese apresenta propostas de metodologias para identificação e controle inteligentes. Uma metodologia para identificação de sistemas dinâmicos não-lineares no tempo discreto, baseada no método de variável instrumental e no modelo nebuloso Takagi-Sugeno, é apresentada. Nesta metodologia, a qual é uma extensão do método de variável instrumental tradicional, as variáveis instrumentais escolhidas, estatisticamente independentes do ruído, são mapeadas em conjuntos nebulosos, particionando o espaço de entrada em subregiões, para estimação não-polarizada dos parâmetros do conseqüente dos modelos nebulosos TS em ambiente ruidoso. Um esquema de controle adaptativo gain scheduling baseado em redes neurais, sistemas nebulosos e algoritmos genéticos para sistemas dinâmicos não-lineares no tempo discreto também é apresentado. O controlador nebuloso é desenvolvido e projetado com o uso de um algoritmo genético para satisfazer, simultaneamente, múltiplos objetivos. Com o esquema de aprendizagem supervisionada, os parâmetros do controlador nebuloso são usados para projetar um gain scheduler neural para ajuste on-line do controlador nebuloso em alguns pontos de operação do sistema dinâmico; This thesis presents proposals of methodologies for intelligent identification and control. A methodology for nonlinear dynamic discrete time systems identification...

Propostas de metodologias para identificação e controle inteligentes

Ginalber Luiz de Oliveira Serra
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 16/09/2005 Português
Relevância na Pesquisa
45.91%
Esta tese apresenta propostas de metodologias para identificação e controle inteligentes. Uma metodologia para identificação de sistemas dinâmicos não-lineares no tempo discreto, baseada tio método de variável instrumental e no modelo nebuloso Takagi-Sugeno, é apresentada. Nesta metodologia, a qual é uma extensão do método de variável instrumental tradicional, as variáveis instrumentais escolhidas, estatisticamente independentes do ruído, são mapeadas em conjuntos nebulosos, particionando o espaço de entrada em sub-regiões, para estimação não-polarizada dos parâmetros do conseqüente dos modelos nebulosos TS em ambiente ruidoso. Um esquema de controle adaptativo gain scheduling baseado em redes neurais, sistemas nebulosos e algoritmos genéticos para sistemas dinâmicos não-lineares no tempo discreto também é apresentado. 0 controlador nebuloso é desenvolvido e projetado com o usa de um algoritmo genético para satisfazer, simultaneamente, múltiplos objetivos. Com o esquema de aprendizagem supervisionada, os parâmetros do controlador nebuloso são usados para projetar um gain scheduler neural para ajuste on-line do controlador nebuloso em alguns pontos de operação do sistema dinâmico; This thesis presents proposals of methodologies for intelligent identification and control. A methodology tor nonlinear dynamic discrete time systems identification...

Yield and kinetic parameters estimation and model reduction in a recombinant E. coli fermentation

Rocha, I.; Ferreira, E. C.
Fonte: Elsevier Publicador: Elsevier
Tipo: Conferência ou Objeto de Conferência
Publicado em //2004 Português
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A genetic algorithm was used to estimate both yield and kinetic coefficients of an unstructured model representing a high-cell density fermentation of E. coli. The model is composed of mass balance equations with 3 states: Biomass, Glucose, and Acetate. Kinetic equations are based on the 3 main metabolic pathways of the microorganism: glucose oxidation, fermentation of glucose and acetate oxidation. Genetic Algorithms were used to minimize the normalized quadratic differences between simulated and real values of the state variables, by manipulating both yield and kinetic coefficients. Data from real fed-batch fermentation runs were analyzed with this optimization routine, the new parameter set obtained allowing a much better description of the process behaviour when compared to simulations conducted with non-optimized parameters obtained from literature. After parameter estimation, a sensitivity function analysis was applied to evaluate the influence of the various parameters on the state variables biomass, acetate, and glucose. Thus, essential parameters were selected and the model was re-written in a more simplified form that could also describe accurately experimental data.

Application of genetic algorithms to model parameter identification of a recombinant E. coli high-cell density fed-batch fermentation

Rocha, I.; Ferreira, E. C.
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em 24/08/2003 Português
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In this work, a genetic algorithm was used to estimate both yield and kinetic coefficients of an unstructured model representing a fed-batch high-cell density fermentation of E. coli. The model, based on the General State Space Dynamical Model was used to represent the three major metabolic pathways: oxidative growth on glucose, fermentative growth on glucose, and oxidative growth on acetate. The structure of the kinetic equations was derived from literature and adapted to represent experimental results. Genetic algorithms were used to minimize the normalized quadratic differences between simulated and real values of the state variables X, A and W, by manipulating both yield and kinetic coefficients.

Efficient Improvement of Silage Additives by Using Genetic Algorithms

Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.
Fonte: American Society for Microbiology Publicador: American Society for Microbiology
Tipo: Artigo de Revista Científica
Publicado em /04/2000 Português
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The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments...

Crystal Structure Prediction (CSP) of Flexible Molecules using Parallel Genetic Algorithms with a Standard Force Field

Kim, Seonah; Orendt, Anita M.; Ferraro, Marta B.; Facelli, Julio C.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /10/2009 Português
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This paper describes the application of our distributed computing framework for crystal structure prediction (CSP), Modified Genetic Algorithms for Crystal and Cluster Prediction (MGAC) to predict the crystal structure of flexible molecules using the General Amber Force Field (GAFF) and the CHARMM program. The MGAC distributed computing framework which includes a series of tightly integrated computer programs for generating the molecule’s force field, sampling crystal structures using a distributed parallel genetic algorithm, local energy minimization of the structures followed by the classifying, sorting and archiving of the most relevant structures. Our results indicate that the method can consistently find the experimentally known crystal structures of flexible molecules, but the number of missing structures and poor ranking observed in some crystals show the need for further improvement of the potential.

A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine
Fonte: Molecular Diversity Preservation International (MDPI) Publicador: Molecular Diversity Preservation International (MDPI)
Tipo: Artigo de Revista Científica
Publicado em 14/09/2010 Português
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Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach...

Internal Lattice Reconfiguration for Diversity Tuning in Cellular Genetic Algorithms

Morales-Reyes, Alicia; Erdogan, Ahmet T.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 31/07/2012 Português
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Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover, internal reconfiguration allows the adjacency of individuals to be maintained. Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 10/12/2013 Português
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Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

Um algoritmo genético híbrido aplicado à predição da estrutura de proteínas utilizando o modelo hidrofóbico-polar bidimensional

Scapin, Marcos Paulo
Fonte: Curitiba Publicador: Curitiba
Tipo: Dissertação de Mestrado Formato: 2, 96 MB
Português
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This work suggests the use of an evolutionary computation technique known as genetic algorithms (GAs) for predicting protein structures in the 2D HP model. The methodology has the main proposal the use of an enhanced fitness function, which makes use of the radius of gyration concept. Special genetic operators were developed and added to those commonly used in GAs, besides new strategies to aid the algorithm in the search of protein conformations. These changes led to the development of a user-friendly software system, with several graphical resources and result reports, named GANDALF PRED. A certain amount of experiments were done with the objective of evaluating the influence of GA parameters in the result obtained. Two test cases were set to evaluate the proposed methodology. The first used 9 manually defined chains whose maximum number of hydrophobic non-local bonds is known a priori and length varying from 20 to 85 residues. The results were compared to two other implementations available in the literature. In the second, 7 proteins with globular traits were taken from PDB and translated to the HP model. Their lengths vary from 288 to 842 residues. The results were presented and discussed, since no comparison could be done. For both test cases...

Algoritmos genéticos para sintonia simultânea de múltiplos controladores em processos de refino

Swiech, Maria Cristina Szpack
Fonte: Curitiba Publicador: Curitiba
Tipo: Dissertação de Mestrado Formato: 1,27 MB
Português
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This work proposes the use of genetic algorithms to tuning decoupled controllers for multivariable systems for refine process. It is presented a new fitness function for genetic algorithm that considers both ITSE (Integral Time Squared Error) and minimum variance criteria. The proposed technique can be applied to tune different control architectures also including non-linear controllers to process presenting strong interactions among its variables. In order to demonstrate the performance of the proposed method, it is applied to three multivariable processes Wood-Berry Distillation Column, Isopropanol Distillation Column and Fluid Catalytic Cracking (FCC), with the use of PID (proportional-integralderivative) and PD-fuzzy decentralised controllers. This approach shows good performance and can be extend to different kinds of controllers and processes.; Este trabalho apresenta uma metodologia de sintonia simultânea de controladores utilizados em um processo multivariável, através da utilização de algoritmos genéticos e sua aplicação em processos de refino. Propõe-se a utilização de uma função de avaliação do algoritmo genético composta por três parcelas considerando os critérios ITSE (Integral Time Squared Error) e de variância mínima para os sinais de saída e de controle. A metodologia pode ser aplicada na sintonia integrada de diferentes tipos de controladores...

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Silva, Lígia Maria Moura e
Fonte: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Sistemas e Computação; Ciência da Computação Publicador: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Sistemas e Computação; Ciência da Computação
Tipo: Dissertação Formato: application/pdf
Português
Relevância na Pesquisa
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Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Comitês de classificadores são sistemas compostos por um conjunto de classificadores individuais e um módulo de combinação...

Projeto Ótimo de redes de distribuição de água via algoritmos genéticos multiobjetivos

Tebcharani, Ganem Jean
Fonte: Universidade Federal de Mato Grosso do Sul Publicador: Universidade Federal de Mato Grosso do Sul
Tipo: Dissertação de Mestrado
Português
Relevância na Pesquisa
45.91%
Com o crescimento e desenvolvimento urbano, o projeto otimizado de rede de distribuição de água se faz cada vez mais necessário, tendo em vista a limitação de recursos financeiros. Nos últimos anos têm sido considerado como segundo objetivo a maximização da confiabilidade além da minimização de custos. A confiabilidade de redes de distribuição de água é a avaliação da qualidade de serviço de abastecimento, a capacidade de atendimento às vazões e pressões requeridas, ou seja, é a quantificação da habilidade da rede em satisfazer demandas em todas as localizações, com pressões aceitáveis. A relação de custo e confiabilidade é antagônica, pois ao minimizar custos, ocorre a diminuição de diâmetros, prejudicando assim a confiabilidade da rede. E, ao maximizar a confiabilidade isoladamente, a rede ficaria com custo elevado. Sendo assim, o projeto ótimo de uma rede seria uma solução ótima harmônica entre os dois objetivos. O presente trabalho representa um esforço no sentido de implementar algoritmos genéticos multiojetivos para a otimização de redes de distribuição de água, considerando como objetivos a minimização de custos e a maximização da confiabilidade. Um algoritmo genético (AG) é um método de busca baseado em mecanismos da genética natural. O AG tem suas raízes no processo biológico de sobrevivência e adaptação. O resultado é um eficiente algoritmo com a flexibilidade para procurar complexos espaços como o espaço solução de redes malhadas. Com o objetivo de testar a eficácia do modelo proposto no presente trabalho...

Otimização da resposta da potência ativa de um inversor conectado à rede elétrica usando algoritmos genéticos

Maia, Helder Zandonadi
Fonte: Universidade Federal de Mato Grosso do Sul Publicador: Universidade Federal de Mato Grosso do Sul
Tipo: Dissertação de Mestrado
Português
Relevância na Pesquisa
45.91%
Neste trabalho analisa-se o emprego de algoritmos genéticos na otimização da resposta do fluxo de potência ativa de um inversor conectado à rede elétrica. É realizada uma revisão de técnicas de otimização analíticas e heurísticas considerando sua aplicabilidade à metodologia de paralelização baseada em curvas P–w e Q–V. Também é introduzido um modelo discreto baseado em Espaço de Estados cuja acurácia é confirmada frente ao modelo de Pequenos Sinais e a um software de simulação de circuitos. A consistência das respostas obtidas pelos algoritmos genéticos é avaliada através de múltiplas execuções e os melhores e piores resultados verificados experimentalmente. Por fim, discutem-se as causas das divergências entre os resultados de simulação e empírico e medidas que podem ser tomadas para reduzi-las.; This work considers the application of genetic algorithms to the optimization of the active power flow of a grid connected inverter. It reviews analytic and heuristic optimization techniques according to their suitability to the parallelization methodology based on P–w and Q–V curves. A discrete state space based model is also introduced and its accuracy in relation to the Small Signals model and results from a circuit simulator are established. The consistency of the genetic algorithms’ results is assessed through multiple runs and experimentally verifying the best and worst ones. Finally...

Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues

Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/11/2014 Português
Relevância na Pesquisa
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In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well-known for their remarkable generality and versatility, and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

Boolean Networks Design by Genetic Algorithms

Roli, Andrea; Arcaroli, Cristian; Lazzarini, Marco; Benedettini, Stefano
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/01/2011 Português
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We present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with the aim of finding a network such that the attractor it reaches is of required length $l$. In general, any target can be defined, provided that it is possible to model the task as an optimisation problem over the space of networks. We experiment with different initial conditions for the networks, namely in ordered, chaotic and critical regions, and also with different target length values. Results show that all kinds of initial networks can attain the desired goal, but with different success ratios: initial populations composed of critical or chaotic networks are more likely to reach the target. Moreover, the evolution starting from critical networks achieves the best overall performance. This study is the first step toward the use of search algorithms as tools for automatically design Boolean networks with required properties.; Comment: 13 pages, 7 figures, 2 tables

Real-Time and Online Digital-Print Factory Workflow Optimization

Duan, Qing; Agrawal, Mukesh; Chakrabarty, Krishnendu; Zeng, Jun; Dispoto, Gary
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Relatório
Publicado em 30/03/2012 Português
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On-demand digital-print service offers mass customization and exemplifies personalized manufacturing services. We describe a real-time and online optimization technique based on genetic algorithms (GA) for print factory workflow optimization. We have simulated digital-print factory manufacturing activities as a heterogeneous, concurrent and integrated system. The simulation is based on a virtual print factory, which incorporates real factory characteristics such as successive order acceptances, diverse production lines, various resource types and quantities, and stochastic machine malfunctions. The optimization objective is to reduce the number of orders that miss deadlines, balance resource utilization, and ensure just-in time production. The optimization technique has been integrated into the virtual factory as a factory scheduler and resource assignment engine. Significant improvements have been achieved using the GA heuristic compared to baseline methods that are currently implemented in an actual industrial setting.