Search

Abstract The present study applies computational intelligence techniques in the development of a hybrid model composed of Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) (MLP-GA) to estimate and fill in the gaps in the monthly variables of evaporation, maximum temperature and relative humidity to six regions in the state of Rio de Janeiro (RJ), Brazil. The results were evaluated using statistical techniques and compared with results obtained by the Multiple Linear Regression (RLM), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models and also compared with the data recorded by the weather stations. The correlation coefficient (r) between the evaporation estimates generated by MLP-GA with the recorded data showed a high relationship, remaining between 0.82 to 0.97. The average percentage error (MPE) ranged from 6.01% to 9.67%, indicating a accuracy between 90% to 94%. For the maximum temperature generated by MLP-GA the correlation with the recorded data remained between 0.97 to 0.99. It also presented the MPE between 0.95% to 1.57%, maintaining the accuracy of the estimated data between 98% to 99%. The correlation coefficient (r) between the relative humidity estimates generated with the MLP-GA remained between 0.89 a 0.97, the MPE between 1.15% to 1.89%, which guaranteed a rate higher than 98% of correctness in its estimates. Such results demonstrated gains in relation to the other applied models and allowed the accomplishment of the filling of most of the missing values.

A neural network and a genetic algorithm were used in a hybrid method to get the optimal design parameters of an Agave angustifolia Haw. green leaf shredder. First, a prototype of an experimental machine was built using the design parameters recommended by the literature and calculated using linear equations. Then, the shredder prototype was subjected to experiments. The defibration data with different blade adjustments were obtained with experimental values. The data was configured and trained with an artificial neural network to establish a correlation between the defibration quality and the design parameters. The multi-objective optimization method based on genetic algorithms determined the optimal design parameters of the shredder’s functional mechanical elements. The best point was obtained from the least number of broken fibers (2.83%) and the most waste (73.15%). The method used proved suitable to optimize the design parameters; this was based on actual data obtained by experiments performed with the prototype and then modeled through artificial intelligence methods such as neural networks to determine an optimal solution using evolutionary genetic algorithm methods.

Inverse source reconstruction problems offer great potential for applications of interest to engineering, such as the identification of polluting sources, and to medicine, such as electroencephalography, to cite at least two relevant examples. From a mathematical point of view, the identification of a concentrated source (intensity and location) corresponds to the identification of the centroid (location) and size (intensity) of a distributed source. On the other hand, from a numerical point of view, it is observed that the use of domain discretization methods is intrinsically associated with the introduction of numerical noise in reconstruction algorithms, which is strongly inadvisable since inverse problems are reckoned to be ill-posed. The objective of this work is to explore, in the context of a Poisson problem and taking into account a numerical point of view, a new reconstruction algorithm based on the method of fundamental solutions, where a source point adequately represents the pointwise source within the domain. The inverse problem is reformulated as an optimization problem solved through a genetic algorithm. Finally, numerical examples are performed to analyze the accuracy of the proposed algorithm for two and three dimensions.

The main goal of this research activity is 3D data analysis and interpretation to study the geometric shaping of architectural elements belonging to Cultural Heritage moving from Point Cloud Semantic Segmentation to 3D modeling. We are testing Evolutionary Computing (EC) to study these shapes merging critical human-driven interpretation and Genetic Algorithms (GAs) driven interpretation. GAs allow to automate identification of open or closed polycentric curves (e.g. ovals) and of analytic curves (e.g. ellipse) starting from reality based planar sections (irregular polylines) extracted from point clouds. GAs, based on natural selection, are tools for solving both constrained and unconstrained optimization problems not well suited for standard algorithms. Moving from theoretical knowledge and treaties study, the research topic is the shape analysis and restitution of hemispherical domes profile (‘pointed arches’ or polycentric arches), i.e. ‘pointed domes’, and of ovoidal/ellipsoidal domes (generic and revolution surfaces). Starting from rules (from centers to points along profiles), our aim is to test a process to generate polycentric (closed or open) and elliptic profiles nearest to reality-based sections (from point along reality-based profiles to centers layout). The Evolutionary Solver (Galapagos component, GH, Rhino) starts from the identification of a given number of points (GENE POOL, i.e. variables) along the irregular reference polyline to select the best solutions (SELECTION) towards the optimal solutions (continuous semi-ideal profile) that best fit the reference curve by minimizing or maximizing different comparison conditions between the two curves (FITNESS).DOI: https://doi.org/10.20365/disegnarecon.26.2021.19

Currently, one may find large repositories on the Internet that can be used as a source of resources for e-learning. In order to facilitate the use of these contents, they are organized as Learning Objects (LOs), which, in addition to the content itself, they hold several metadata to describe them. Due to the large volume of materials available, it becomes difficult to choose those LOs that best meet the needs of each individual. To suggest the most suitable LOs, we rely on Recommendation Systems, that are responsible to find and recommend the most useful items to students. In this context, the present work proposes a multiobjective optimization approach for the LOs recommendation problem. In this problem, there is a set of concepts to be taught, according to an Instructional Design, a repository of LOs, in which each LO addresses one or more concepts, and also pedagogical dependencies, that enforce certain concepts to be presented in advance to others. In this problem, we do not seek for solutions that necessarily cover all established concepts. In fact, we aim to obtain solutions that fulfill two distinct objectives, namely: i) lower cost, given by the sum of the cost of each LO present in the solution; ii) greater number of concepts covered, respecting the established pedagogical dependencies. To solve the problem, we used the NSGA-II, a multiobjective Genetic Algorithm implemented in the MOEA framework. The problem instances were artificially generated, simulating repositories of 1000 LOs, courses with 200 concepts, with up to 20 dependencies. The results obtained covered sets of different configurations of solutions, with different numbers of concepts covered and costs of the solutions, which brings greater possibilities to deliver content in accordance with the objectives, needs and reality of the students. [ABSTRACT FROM AUTHOR]

Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, and Alpine) are used as a study case. Further, four different crossover operators (simple, arithmetical, non-uniform arithmetical, and Linear), two selection mechanisms (roulette wheel and tournament), and two mutation operators (uniform and non-uniform) are shown. Results indicate that non-uniform mutation and tournament selection tend to present better outcomes.

ABSTRACT The objective of this research was to simulate the genetic gains expected comparing random mating strategies and mate selection by optimum contribution with different penalty levels in the inbreeding rate of Santa Inês sheep. The optimum contribution theory was thus applied to optimize genetic gain in the long term in twelve selection groups by selectively mating 500 females with the respective males, increasingly penalizing the increase in inbreeding in the objective function. Genetic algorithms were used to find the optimum contribution. Optimization was achieved via EVA software. Selection candidates had their contribution defined into four treatments, using different values to weigh the genetic merit and penalize increases in inbreeding. This made it possible to measure the degree of control over those parameters that can be obtained with this methodology. This selection offers different levels of genetic gain, which are achievable from restrictions on the coancestry. The number of males selected and their distribution into selection groups varied according to the penalty attributed to inbreeding in the objective function. Mate selection using optimum contribution should be adopted when aiming to limit the increase in inbreeding. Increasing the exchange of genetic material between groups is recommended to elevate genetic gain and maintain control over inbreeding.

This work investigated the Artificial Intelligence planning for pedagogical actions sequencing, according to the student's profile. The actions represented the cognitive process given by Bloom's Taxonomy (BT) and the student profile was modeled by the Revised Approaches to Studying Inventory (RASI). To measure the suitability of a sequence to the student's profile, it was necessary to map these two theories, this mapping being one of the contributions of this study. Thus, the sequencing of actions was formulated as an optimization problem and developed through Genetic Algorithm. The proposition of the function to be optimized for this problem is also a contribution, since establishing criteria to evaluate pedagogical aspects has been a challenge for Informatics in Education. Experiments carried out had 41 participants who answered the RASI inventory and, after receiving and analyzing the sequences of actions generated by the planner proposed in this work, they also answered a satisfaction questionnaire about the sequence. The results obtained can be considered promising, demonstrating the feasibility of the research. [ABSTRACT FROM AUTHOR]

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One field of the most fertile in the study of the strategies is the Theory of Games, which aims to capture the behavior of individuals in strategic situations. This paper aims to analyze the evolution of strategies of suppliers and borrowers credit by proposing two simultaneous and reflective games, making, for both, computational simulations. Two games were held simultaneously, where the results of an influence the pay-offs of each other. After 400 generations, it's believed that this is due to the complex interaction between the two groups of actors and is not seen as a setback by the authors of the study, whereas data on the evolution of strategies is fertile ground for valuable analysis on the interaction of actors in this study. The conclusions show the potential of techniques for analyzing and understanding the strategic behavior of SMCs in their relationship with suppliers of large companies. [ABSTRACT FROM AUTHOR]

Machine Learning (MA) has the concepts of development of computational techniques, construction of a system capable of acquiring automatic knowledge and decision making alone, have paradigms such as symbols, statistical, example and connectionist. Genetic Algorithm (GA) works with concepts on the field of evolutionary computation, are methods of optimization and seek inspiration in the mechanisms of evolution of species, with intuitive knowledge and deductive are key parts of the genetic algorithm. Without the pretension to present the historical chronology of the industry 4.0, AM and AG, the importance of concepts on the subject is verified, search through researches of scientific articles, didactic material and books, subjects for understanding of how one arrives in the industry 4.0. [ABSTRACT FROM AUTHOR]

This work presents a hybrid approach called GA-NN for solving the Capacitated Vehicle Routing Problem (CVRP) using Genetic Algorithms (GA) and Nearest Neighbor heuristic (NN). The first technique was applied to determine the groups of customers to be served by the vehicles while the second is responsible to build the route of each vehicle. In addition, the heuristics of Gillett & Miller (GM) and Downhill (DH) were used, respectively, to generate the initial population of GA and to refine the solutions provided by GA. In the results section, we firstly present experiments demonstrating the performance of the NN heuristic for solving the Shortest Path and Traveling Salesman problems. The results obtained in such experiments constitute the main motivation for proposing the GA-NN. The second experimental study shows that the proposed hybrid approach achieved good solutions for instances of CVRP widely known in the literature, with low computational cost. It also allowed us to evidence that the use of GM and DH helped the hybrid GA-NN to converge on promising points in the search space, with a small number of generations.

A bi-factorial experimental design was considered to assess moisture variation of sweet potato-quinoa-kiwicha flakes (SP-Q-K) caused by the changes in the rotational speed and steam pressure of a rotary drum dryer (RDD). As it is a design with discrete variables, there is a limitation in the modeling and optimization thus techniques of Artificial Intelligence (AI): Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GA), were applied, and their prediction ability evaluated. Due to the limitation of data for proper training, the ANN did not allow a correct prediction of the experimental data. Response Surface Methodology (RSM) was employed to obtain the relational equation among the experimental variables, which was used as the objective function with GA, and this allowed moisture optimization. Because of this, it is recommended to integrate RSM and GA into optimization studies. In this research the use of FL among variables, enabled us to get the best prediction adjustment of experimental values (R2 = 0.99), with a mean absolute error of 0.6±0.66 %, setting a pressure value of 5 atm and a speed value of 6 rpm for flakes at 4.99 % humidity.

Los intercambiadores de calor de tubo y coraza son de los equipos más importantes en la industria. Su diseño termodinámico se basa en el coeficiente global de transferencia de calor y la caída de presión total. En 2007 se estableció una nueva propiedad termodinámica denominada “Entransía”, que expresa la capacidad de un cuerpo de transferir calor. A la pérdida de esa capacidad se le denomina “Disipación de Entransía”. Para evaluar el impacto ecológico de las máquinas térmicas, Angulo-Brown creó en 1991 la llamada función ecológica. En el presente trabajo se combinó la disipación de entransía con la función ecológica y se creó una nueva expresión para evaluar el impacto ambiental de los intercambiadores de calor. Se realizó además la optimización muti-objetivo de estos equipos. Fueron utilizadas como funciones objetivo la función ecológica y el costo. Para realizar la optimización se utilizó el método de los Algoritmos Genéticos.Shell and tube heat exchangers are ones the most important equipment in the industry. Their thermodynamic design is based on the global heat transference coefficient and the pressure drop. In 2007 was settled a new thermodynamic property denominated “Entransy”, which expresses the capacity of a body to transfer heat. The loss of this capacity is denominated “Entransy Dissipation”. For evaluating the ecological impact of thermal machines, Angulo-Brown created in 1991 the “ecological function”. In this paper the“entransy dissipation” and the ecological function were combined and a new expression for evaluating the ecological impact of shell and tube heat exchangers was created. A multi-objective optimization of this equipment wasalso realized. The ecological function and the cost wereused as objective functions. For carry out the optimization the method of the Genetic Algorithms was used.

La cobertura de la demanda de energía eléctrica mediante distintas fuentes de generación es un problema técnico de gran importancia. En concreto, este problema es de gran interés en la titulación de los Ingenieros Industriales, los cuales dentro de su formación abordan las diferentes tecnologías de generación de energía y el mercado eléctrico. El trabajo concreto se centra en optimizar la curva de demanda diaria de energía suponiendo que se tienen dos tipos de centrales: térmicas e hidráulicas. Para cada tipo de central se desarrolla un modelo matemático sencillo y se plantean las diferentes restricciones de suministro que tienen las distintas centrales. Para la optimización se ha utilizado un algoritmo genético, con la finalidad de dar a conocer a los alumnos una técnica heurística de optimización, y el proceso por el cual encuentra la mejor solución.

The energy efficiency of an electric system depends in great measure of the reactive power compensationthat it is carried out. One of the roads more employees are the use of capacitors banks.A good selection of these, presupposes three aspects: the selection of the banks capacity, thecompensator type to use, as well as the location in the low voltage system.References of methods thatconsider the three aspects in a same problem are based on the mathematical programming, but thesecannot represent all the effects of the compensation and these based in technical of continuousprogramming, being this a complex optimization problem of discreet search space.In the paper, we havedeveloped a method with the employment of the genetic algorithms, which facilitates a more exact problemformulation. The method has been programmed in MATLAB, version 7.5 (R2007b), using programmingstructures of Genetic Algorithm and Direct Search Toolbox.The effectiveness is shown in an example ofreal application to the low voltage system of a hotel installation of the Cubanacan Company in Camagüey.

O objetivo deste trabalho é a extração semiautomática de estradas vicinais. Este estudo é dividido em duas fases distintas. Na primeira fase é proposto um método para determinar larguras de estradas; na segunda fase é proposta uma função de aptidão para algoritmos genéticos que possibilita a detecção de segmentos candidatos a eixo deestradas vicinais. Três imagens digitais são usadas na realização dos experimentos. Para o estudo, a validação dos resultados obtidos foi realizada a partir de uma imagem de referência. A imagem de referência foi criada por meio de vetorização manual. O julgamento da acurácia foi computado com base nos índices: completeza, correção eRMS. Os resultados obtidos apontam a metodologia proposta como promissora. The aim objective of this paper is to present a semi-automatic extraction of local roads. The research is divided in two different phases. In the first one, a method to determine road width is proposed; in the second one, a fitness function for genetic algorithms is proposed. The referred function makes possible the detection of candidate segments for local roads axis. Three digital images are used in conducting the experiments. For the study, the validation of the obtained results was accomplished based on a reference image. The reference image was created through manualvectoring, and an accuracy evaluation was computed based on the following indexes: completeness, correctness and RMS. The obtained results point out the proposed methodology as promising.

En el presente artículo se muestra un proceso de optimización implementado usando algoritmos genéticos. La población inicial del AG está compuesta por 128 cromosomas con 11 genes por cromosoma. Los cromosomas del AG están compuestos por las longitudes y separaciones de los elementos de la antena Yagi-Uda; los rangos de estos genes fueron escogidos siguiendo estándares de diseño para dichas antenas. Los genes pasan un proceso de análisis para medir cada una las antenas de cada generación de del AG para asignar la aptitud de los individuos. Con el fin de verificar los resultados obtenidos, se aplicaron varias pruebas, entre ellas la construcción de una antena Yagi-Uda optimizada a la cual se le midieron y verificaron sus características electromagnéticas.This paper describes an optimization process implemented using Genetic Algorithms. The initial population of the GA is composed of 128 chromosomes with 11 genes per chromosome. The chromosomes of the GA are composed by the length and separations of the elements of the Yagi-Uda antenna; the ranks of this genes where chosen by design standards for such antennas. All genes undergo a process of analysis to assess every one of the antennas of each generation of the GA to assign the fitness of the individuals. In order to verify the obtained results, various tests were made, and among them excel the construction of the optimized Yagi-Uda antenna to measure and verify it electromagnetic characteristics.

This article introduces the application of genetic algorithms to several research areas, from music to the oil industry.

This study shows the importance of genetic algorithms in the application of computational problems extremely difficult to resolve due to an impractically large number of solutions. The genetic algorithms - GA are based on nature to generate optimal solutions to difficult problems to be solved computationally in which a population of individuals is created and submitted to genetic operators: selection, crossover and mutation in order to generate a process similar to the evolution these natural reaching a satisfactory solution of the problem in question. An extremely interesting and complex problem is the cleavage of proteins, which either is to find rules that involve combinations of amino acid sequences of various proteins. This is a problem with many solutions, because the number of combinations position / amino acid is proportional to the factorial of the number of positions and amino acids. Following the guidelines of the theory of evolution is a family of algorithms used to solve problems. The structures are organized following an abstract model of data and the test is done with a sequence fictitious.

O projeto de arranjo físico modular tem como base o agrupamento de máquinas em módulos a partir de subseqüências de operações comuns a um conjunto de peças. No método proposto por Huang (2003), os módulos de arranjo físico são gerados por análise de agrupamentos. O presente trabalho apresenta uma forma alternativa à geração de módulos de arranjo físico, através de algoritmo genético de agrupamento (AGA). O AGA permite ao usuário especificar a priori o número desejado de módulos (problema de K-agrupamentos), e também trabalhar de forma que o número e a formação dos módulos sejam variáveis de decisão do problema (problema de agrupamento automático). Uma característica importante do AGA é sua flexibilidade, pois fornece a possibilidade ao usuário de interagir com o método de solução através da escolha de diferentes codificações do cromossomo, de medidas de similaridade para comparação de seqüências de operações e de métodos de geração da nova população. Para tal foram desenvolvidos dois operadores de cruzamento e três operadores de mutação. No exemplo apresentado, os módulos de arranjo físico gerados pelo AGA e o arranjo físico final da fábrica foram comparados aos de Huang (2003) e houve importante redução da distância total percorrida pelo conjunto de peças, demonstrando a eficácia do algoritmo genético de agrupamento. Dessa forma, os resultados apontaram o AGA como uma alternativa à geração de módulos de arranjo físico em projeto de arranjo físico modular.The modular facility layout consists of grouping machines into modules according to subsequences of operations common to a set of parts. In the method proposed by Huang (2003), facility layout modules are generated by cluster analysis. This paper introduces an alternative way to generate layout modules through a clustering genetic algorithm (CGA). The CGA allows the user to specify the number of modules he wants (K-grouping problem) and to optimize the number of modules (automatic clustering problem). An important feature of the genetic algorithm is its flexibility, which enables the user to interact with the problem-solving method by presenting him with a choice of chromosome codes, measures of similarity to compare sequences of operations, and methods for generating new populations. To this end, two crossover operators and three mutation operators were developed. In the example presented here, the layout modules generated by the CGA and the plant's final physical layout were compared with those of Huang (2003). This comparison revealed a significant reduction in the total distance covered by the set of parts, demonstrating the effectiveness of the clustering genetic algorithm. Thus, the results indicated that the clustering genetic algorithm is an alternative for generating layout modules in the design of modular facility layouts.

In the present paper, a computational systematic procedure for isothermal Reactor Network Synthesis (RNS) is presented. A superstructure of ideal CSTR and PFR reactors is proposed and the model is formulated as a constrained Nonlinear Programming (NLP) problem. Complex reactions (series/parallel reactions) are considered. The objective function is based on yield or selectivity, depending on the desired product, subject to different operational conditions. The problem constraints are mass balances in the reactorsand in the considered reactor network superstructure. A systematic computational procedure is proposed and a Genetic Algorithm (GA) is developed to obtain the optimal reactor arrangement with the maximum yield or selectivity and minimum reactor volume. Results are as good as or better than those reported in the literature.No presentetrabalho apresenta-se um procedimento computacional para síntese de redes de reatores (SRR) operando em condições isotérmicas. Uma superestrutura de rede de reatores formada por reatores ideais CSTR e PFR é proposta e o problema apresenta uma formulação de programação não linear (PNL). São consideradas reações complexas (série/paralelas). A função objetivo é baseada no rendimento ou na seletividade em relação ao produto desejado, sujeito a diferentes condições de operação. As restrições ao problema são provenientes dos balanços de massa e da configuração da superestrutura considerada.No procedimento computacional é proposto um Algoritmo Genético (AG) para obtenção do arranjo ótimo de reatores com máximo rendimento ou seletividade com menor volume reacional. Os resultados obtidos são condizentes com os obtidos na literatura.

Este artigo apresenta um sistema de suporte à decisão para a otimização de alternativas de desenvolvimento de campos de petróleo. O desenvolvimento de um campo de petróleo consiste na escolha de uma alternativa para a explotação de um reservatório petrolífero, conhecido e delimitado, que permita produzir a maior quantidade de hidrocarboneto possível, dentro dos limites físicos e econômicos existentes, isto é, maximizar o valor presente líquido (VPL). O VPL é calculado a partir da produção de óleo, a qual é obtida com o uso de um simulador de reservatório. Cada execução do simulador pode demorar desde alguns segundos até várias horas, dependendo da complexidade do reservatório modelado. Isto reduz o número de alternativas que podem ser geradas e avaliadas pelo especialista na busca da melhor solução. Deste modo, o objetivo deste trabalho é propor e avaliar um sistema inteligente de otimização que emprega: algoritmos genéticos (AGs), algoritmos culturais (ACs) e coevolução para a busca de uma alternativa de desenvolvimento ótima; e um ambiente de computação paralela para a simulação de reservatório e cálculo do VPL das alternativas. O sistema resultante permite que o especialista obtenha, em tempo hábil, a alternativa ótima (ou quase-ótima) para o desenvolvimento de um campo de petróleo conhecido. Os resultados obtidos nos estudos de casos apresentados demonstram que o sistema proposto, baseado em técnicas inteligentes, obtém boas alternativas de desenvolvimento de campos petrolíferos com uma grande redução do tempo computacional, redução esta obtida a partir do aproveitamento do poder computacional de um ambiente de computação paralela e do aproveitamento de conhecimento do especialista, por meio das sementes iniciais.This paper presents an optimization system for the development of petroleum fields. Developing a petroleum field consists of choosing an alternative exploitation of an already known and delimited petroleum reservoir allowing the maximum hydrocarbon production within the physical and economical limitations i.e., maximizing the net present value (NPV). The net present value is calculated according to the oil production, which is obtained with the use of a reservoir simulator. Each reservoir simulation can take from few seconds to several hours, depending on the complexity of the reservoir being modeled. This reduces the total number of configurations that can be generated and evaluated by the user in search for the best solution. Therefore, this work proposes and evaluates a new intelligent, optimization system that employs genetic algorithms (GA), cultural algorithms (CA), and co-evolution in order to search for an optimal development alternative in a parallel computing environment for reservoir simulations and NPV calculation. The proposed system provides the user, in a reasonable time, with the optimum (or sub-optimum) configuration for the development of the petroleum field. The results obtained in the case studies demonstrate that the proposed system, based on intelligent techniques, enable good configurations for the development of petroleum fields with a great reduction in computational time. This reduction is obtained from the computational power of the parallel computing environment and from the expert knowledge, through the initial configuration of the optimizing system (initial seed).

New Product Development (NPD) is recognized as a fundamental activity that has a relevant impact on the performance of companies. Despite the relevance of the financial market there is a lack of work on new financial product development. The aim of this research is to propose the use of Genetic Algorithms (GA) as an alternative procedure for evaluating the most favorable combination of variables for the product launch. The paper focuses on: (i) determining the essential variables of the financial product studied (investment fund); (ii) determining how to evaluate the success of a new investment fund launch and (iii) how GA can be applied to the financial product development problem. The proposed framework was tested using 4 years of real data from the Brazilian financial market and the results suggest that this is an innovative development methodology and useful for designing complex financial products with many attributes.

En este trabajo se desarrolla el modelado dinámico de lámparas de descarga de alta intensidad (HID en inglés), de manera que pueda contribuir al desarrollo posterior de un sistema de control avanzado sobre la etapa de potencia de un balasto electrónico, que permita operar este tipo de lámparas en alta frecuencia. El modelo tiene en cuenta la presencia del fenómeno de la resonancia acústica (RA), que usualmente se presenta en las lámparas HID a frecuencias elevadas. Para el modelado se emplean técnicas híbridas, teniendo como punto de partida las ecuaciones de balance de energía en el interior de la lámpara, en tanto para la parametrización, se aplica identificación de sistemas y optimización mediante algoritmos genéticos. La implementación del modelo se realiza con el software Matlab R2011a. Como resultado se obtiene un modelo dinámico para lámparas HID en alta frecuencia, validado para lámparas de alta presión de sodio.In this paper the dynamic modeling of high intensity discharge lamps (HID) is developed. This will contribute to the further development of an advanced control system on the power stage of an electronic ballast, which allows these lamps operate at high frequency. The model must take into account the presence of the acoustic resonance’s phenomenon (AR), which usually occurs when HID lamps operates at high frequency. Hybrid modeling techniques were employed, the balance equations and the empirical expressions were obtained and the model was parameterized by identification techniques and optimization based on genetic algorithms. The implementation of the model has performed using Simulink tool of Matlab R2011a software. As a result, a dynamic model for HID lamps in high frequency has achieved and validated for high-pressure sodium lamps (HPS).

Una óptima selección de estos, presupone tres aspectos: la selección de la capacidad de los bancos, el tipo de compensador a utilizar, así como la ubicación en el sistema eléctrico de baja tensión Existen referencias de métodos que consideran los tres aspectos de manera conjunta, basados en la programación matemática, pero estos presentan limitantes dado que no pueden representar todos los efectos de la compensación y se basan en técnicas de programación continua, siendo este un problema de optimización complejo de espacio de búsqueda discreto.En tal sentido, hemos desarrollado un método con el empleo de los algoritmos genéticos que posibilita una formulación más exacta del problema. El método ha sido programado con ayuda del MATLAB, versión 7.5 (R2007b), utilizando las estructuras de programación de Genetic Algorithm and Direct Search Toolbox. La efectividad del mismo se muestra en un ejemplo real de aplicación al sistema eléctrico de baja tensión de una instalación hotelera del Grupo Cubanacan de la provincia de Camagüey. A good selection of these, presupposes three aspects: the selection of the banks capacity, the compensador type to use, as well as the location in the low voltage system.References of methods that consider the three aspects in a same problem are based on the mathematical programming, but these cannot represent all the effects of the compensation and these based in technical of continuous programming, being this a complex optimization problem of discreet search space.In the paper, we have developed a method with the employment of the genetic algorithms, which facilitates a more exact problem formulation. The method has been programmed in MATLAB, version 7.5 (R2007b), using programming structures of Genetic Algorithm and Direct Search Toolbox.The effectiveness is shown in an example of real application to the low voltage system of a hotel installation of the Cubanacan Company in Camagüey.

The Chinese Postman Problem is an optimization problem that aims to cover all the arcs of a graph, minimizing the total distance traveled. Can be applied to non-directed graphs (two-way streets), directed (one-way streets) or mixed (some two-way streets and other one-way). These arch for the route is done by algorithms that generate approximate solutions. In this work we used a genetic algorithm for the construction of routes that approximate the optimal solution. The main objective is to minimize the cost of the course of collection and transportation solid waste in the city of Irati (PR), Brazil. The application of the algorithm was performed in a downtown area. Were used formapping, real data courtesy of City Hall officials. With the help of the online application Google Earth, we obtained the geographical coordinates of the vertices of the graph associated with the problem. The results generated by the genetic algorithm were compared with the optimal solution obtained by LINGO ® 12.0 software. These results were satisfactory for this mall instance of problems analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Applied Economics / Economía Aplicada is the property of FEA-RP, Universidade de Sao Paulo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Gestão & Tecnologia is the property of Revista Gestao & Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Administraçãao da UNIMEP is the property of Revista de Administracao da UNIMEP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Administração e Inovação (RAI) is the property of Revista de Administracao e Inovacao- RAI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Scientiarum: Technology is the property of Universidade Estadual de Maringa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Scientia is the property of Universidade do Vale do Rio dos Sinos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)