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There are classical methods to determine the synthesis of mechanisms based on graphical and analytical procedures in which the main limitation is that the number of precision points that can be taken into account to define the desired trajectory is restricted. This paper describes the development of a software that performs trajectory generation synthesis and kinematic simulation of four-bar flat mechanisms for multiple precision points, using Genetic Algorithms as an optimization method. In addition, position, velocity and acceleration graphs are generated to simplify the interpretation of the data and to calculate the proximity between the points through which the obtained mechanism will pass. Initially, a flow diagram was developed in Java language, where the algorithmic logic of its operation is defined. Then, a flexible, intuitive, and user-friendly graphical interface was developed and validated by presenting it to potential end users. The results obtained were validated by comparing them with different mechanisms proposed in previous studies, demonstrating that the software will allow users to perform the synthesis of trajectories easier and faster than existing analytical methods. [ABSTRACT FROM AUTHOR]

Pulse code modulation (PCM) is an essential technique in digital communications, which transforms analog signals into digital data for transmission and processing. This technique is based on three fundamental processes: sampling, quantization and coding. With the growing demands on communication efficiency and quality, the optimization of PCM systems becomes crucial. This article explores advanced methods for the design and optimization of PCM systems, highlighting the use of sophisticated tools and algorithms to improve the overall performance of these systems. Approaches for bandwidth reduction, signal quality improvement, and error minimization are addressed. Innovative techniques such as variable pulse length coding and the use of optimization algorithms, such as genetic algorithms and the particle swarm optimization algorithm, are presented. Through practical case studies, it is demonstrated how these techniques can be applied to obtain superior results on real systems. This paper provides a comprehensive overview of advances in PCM modulation and its application in digital communication enhancement, offering recommendations for future research and applications in high-demand environments. [ABSTRACT FROM AUTHOR]

Optimal distribution networks reconfiguration is very important in reducing losses and operating costs and in improving networks security, stability and reliability indicators. The work objective is reprogramming an algorithm that improves results obtained previously. Paper presents non-dominated classification genetic algorithm II parts adaptation and reprogramming, with a new coding that, unlike previous authors, avoids modified genetic operators and take better advantage of algorithm's potentialities. In addition, graph theory is used to transform non-feasible individuals and avoid new generations of them, dispensing with annoying mesh checking and reducing the search space and computational burden. Proposal's effectiveness was tested with 33 and 70 bars test distribution systems, showing promising results and better than previous proposals. [ABSTRACT FROM AUTHOR]

In this study we considered the optimization problem that integrates the logistics operations of location, routing and multiperiod inventory in a two echelon distribution network, named 2eLIRP. In this paper we propose a new hybrid metaheuristic based on Tabu search, genetic algorithms and the nearest neighbor heuristic. The performances of our technique was compared with classics metaheuristics and the results of the experiments carried out in 96.67% were better. [ABSTRACT FROM AUTHOR]

In this paper, a multi objective optimization of asymmetric spur gear transmission is carried out using the modified Nondominated Sorting Genetic Algorithm II (NSGA II). To achieve this goal, three pairs optimization problem are formulated: minimum tooth stress and minimum transmission volume, minimum tooth stress and maximum coating factor, minimum transmission volume and maximum robustness index. Constraints considered include wheel tooth thickness, correction coefficient, and constant center distance. The optimization procedure used provides functions to find the parameters that minimize or maximize the objective functions respecting the restrictions. As results, optimal values are obtained for the robustness index and tooth stress, demonstrating the validity of the procedure used for its easy adaptation to complex mathematical models. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm using CSi Application Programming Interface (CSi API), which allows the automation of the structural optimization process for plane frames, linking powerful computational tools such as SAP2000v16 and Matlab. In addition, aspects usually ignored in modelling and structural analysis for structural optimization are included, such as the influence of cracking of concrete elements in their stiffness and the inclusion of a second order analysis. On the other hand, due to the complexity of the objective function, it was necessary to use metaheuristic optimization methods: a hybridization between genetic algorithms and the Nelder-Mead algorithm, instead of the classical methods used in previous studies, obtaining satisfactory results. The results are expressed in the form of design recommendations and they are compared with those obtained in previous investigations. [ABSTRACT FROM AUTHOR]

The Job Shop scheduling problem has been extensively studied in the last decades due to its importance and computational complexity. However, a large part of the papers address the static and deterministic version of the problem. This work presents an integration of a simulation model with an optimization method in order to solve the dynamic job shop scheduling problem. The proposed model integration is accomplished using out-of-process components, through the ActiveX Automation technology and the Visual Basic for Application, in which a simple Genetic Algorithm runs as a free-standing application. Results of the proposed method were compared with some common dispatching rules and showed that the proposed approach the scheduling problem efficiently. Moreover, the solutions generated by GA are less sensitive to variations in demand, which is quite significant in such environments. [ABSTRACT FROM AUTHOR]

The implementation of trading strategies through computational tools and artificial intelligence, such as artificial neural networks (ann) and genetic algorithms (ag), have presented important advances in the last years, In this paper it is implemented a ag for the optimization of a trading strategy with two moving averages in the inter-day market of crude oil futures. The objective function is the global return of the investment. The document presents the methodology and the design of this investment strategy with consistent results even with out-of-sample data. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of metaheuristics applied to the power system interdiction problem. This problem considers the interaction of a disruptive agent and a system operator. The disruptive agent aims at maximizing the damage of the power system, measured as load shedding. This agent has limited resources and must select the combination of elements to attack that result in the greatest damage to the system, anticipating the reaction of the system operator. On the other hand, the system operator must minimize the load shedding by redispatching generation resources. The interdiction model is nonlinear, nonconvex and multimodal. The solutions provided by three metaheuristic techniques are compared: Genetic Algorithm, GRASP and Iterated Local Search. Results show that the Iterated Local Search adapts better to the type of problem and allows obtaining the best rate between quality of solutions and computation time. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology based on decentralized operation and coordination of large power systems, obtaining the system's optimization in a simplified way, and reducing time consumption. The optimal search of generator voltage, tap changer and compensation, is accomplished by using an evolutionary technique, and considering the problem as a multi-objective optimization case. In this paper, both the decomposition network and the optimization problem are applied to two study cases (39 and 190 buses). Global optimal operation is attained, preserving the voltage stability and the exchanges among areas, with minimal operational information, simplicity in the equivalents and time consuming reduction. Results are similar to those obtained by a centralized optimization strategy. [ABSTRACT FROM AUTHOR]

Canonical Correlation Analysis (CCA) is an exploratory method of Multivariate Analysis, it studies the relationship between two sets of quantitative variables observed in the same set of individuals. It obtains new variables that are linear combination of the original variables of the two groups, such that the correlation between the projections of the data of this new variables is maximum. There are several proposals to determine canonical correlations and canonical vectors using techniques of numerical linear algebra, that is, in the formulation of Lagrange, CCA becomes a generalized eigenvalues and eigenvectors problem, assuming that the variance-covariance matrices are invertible. On the other hand, Genetic Algorithms (GA) are adaptive methods used to solve global optimization problems. When the invertibility condition is not satisfied by the variance-covariance matrices, we propose in this paper, use GA to solve the problem of CCA directly from the definition. We test the proposal with problems reported in the literature and also presents a real application of CCA to data of soil carbon in the area of Teziutlan, Puebla. [ABSTRACT FROM AUTHOR]

In this paper an innovative focus to programming Job Shop systems, based on sustainability, is presented. The following sustainability variables are evaluated: carbon dioxide emissions (environmental variable), sound intensity, accident rate and handled weight (social variables: occupational health) and finally, makespan time and direct labor costs (economic variables). The methodology is structured in fourteen steps that cover aspects such as multiobjective method design, description of genetic algorithms, and comparison with other methods. It is determined that the designed method shows greater performance for the variable weighted factor P(Xj) when they are compared with results from other methods such as programming rules and mobile bottle neck method. [ABSTRACT FROM AUTHOR]

The placement of convex objects in the plane, specially rectangular objects has been studied extensively in the literature and its applications to VLSI designs, the steel industry (stock cutting). The placement of non convex objects and its application in many design and textile industries, specially the textile industry has been less studied. Mathematical Approaches determining and adequate solution of such problems include linear programming and the newest genetic algorithms. In this paper we consider a particular industrial problem, the design of printing sheets for decorative porcelain. These objects permit inclusions and other geometric situations which do not appear in the case of convex objects. In this paper we proof results related with this problem for different algorithms: a heuristic algorithm, simulated annealing algorithm and a genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper is shown the simulation results of a new heuristic, called snake-one. The simulation was performed in the National Science Foundation NETwork (NSFNET-USA) under dynamic traffic and compared with other heuristics such as Simulated Annealing, Genetic Algorithms and Tabu Search previously released, using the Blocking Probability indicator and Network Utilization. Comparing these heuristics, we can observe the improvement, of the blocking up to 130 Erlangs. Nevertheless, this results mean an increased and sustained use of the network. This behavior determines a partially good result, which determines the study of a modification of the Snake-one algorithm to improve both indicators. [ABSTRACT FROM AUTHOR]

A fuzzy classifier is a system that assigns a class label to object based on a description of it and makes use of fuzzy sets as part of its operation. Its functioning can be optimized using genetic algorithms, ensuring viable solutions. The paper proposes a fuzzy classifier optimized using a genetic algorithm hybridized with a fuzzy clustering technique. A prototype is implemented and evaluated with synthetic reference data sets as possible classification problems treatments for malignant neoplastic diseases. The results are compared with other classifiers found in the literature on the same test data. The conclusion is that the proposed method obtained similar results with functions easier to interpret. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of optimal location and sizing of distributed generation in distribution systems. For such an assessment the following search techniques were implemented: i) simulated annealing, ii) variable neighborhood search, iii) genetic algorithm and iv) hybrid genetic algorithm. Two objectives have been considered in the optimal location and sizing problem: the minimization of electric power losses and the improvement of voltage profile. To compare the effectiveness of the methodologies a 34 bus distribution test system is used. The correct location and sizing of the distributed generation allowed substantially improving the voltage profile of the network and reducing in 80% the power losses in the test system. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile. [ABSTRACT FROM AUTHOR]

This paper considers a production environment of a flexible flowshop with anticipatory sequence-dependent setup times. A genetic algorithm improved to minimize total tardiness is presented. The generation of the initial population is performed using the heuristics EDD (Earliest Due Date) and Slack neighborhoods, also considering further search in IP neighborhood to improve the performance of the proposed genetic algorithm. The results show that the genetic algorithms with initial population generated as EDD (AG_EDD) and Slack (AG_Slack) neighborhood improve the performance of the basic genetic algorithm. The AG_EDD algorithm shows better performance, the feature that remains when incorporating IP neighborhood search. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increase on the demand of online services has generated the need to increase the supply of bandwidth and timely access to transport services in optical networks with WDM (Wavelength Division Multiplexing) technology. The algorithms associated to provide this service, do not achieve an optimal performance under high demand load in dynamic traffic condition. The present paper proposes an integrated strategy for the solution of the DLE (Dynamic Ligthpath Establishment), that is, without dividing the problem, using the Tabu Search algorithm. The results show that the algorithm used works better on dynamic sceneries, but with a higher network utilization. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Optical recognition of handwritten numbers was worked by different methods, with satisfactory results. In this paper, we propose fuzzy systems with genetic algorithms, specifically memetic, to do this task. Results with this method are compared with neuronal network of semi-supervised learning, where we was used networks with radial base functions (RBF). To make the comparison, is possible observed that this kind of neuronal network offers advantages regarding error rates and time-to-results of the recognition system, compared with methods based in fuzzy systems. [ABSTRACT FROM AUTHOR]

This paper presents the design of a three-fingered gripper with two degrees of freedom (DOF) per finger for a total of six (DOF). We show a brief mathematical description of the kinematics involved in the gripper with which to determine the workspace. With the workspace, all possible attachment points are obtained, that will be the points at which the end-effector contacts with the object geometry. To select which of these points is best for hold the object, we proceed to evaluate the force that is exerted on it by mathematical theory called Screw. This force should be the minimum to ensure that do not present sliding as well as avoid damages of the object by excess strength. Due to the fact that the contact points are numerous and evaluate them takes quite time by an algorithm combining, for this reason the optimization algorithm Non-dominated Sorting Genetic Algorithm (NSGA) is implemented. The results of the optimization with attachment points that exert minimal force were presented. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for solving the static planning problem in electrical energy transmission networks considering uncertainty in demand and conductor selection for the transmission lines that belong to the set of all new paths. The optimization problem is solved using a specialized genetic algorithm, which uses the logic of the genetic algorithm proposed by Chu and Beasley, combined with exact optimization. The testing bench for the proposed methodology was chosen to be the Colombian power system of 93 buses and 155 candidate lines. The results obtained enhance the traditional solution for the Colombian power system regarding the transmission network planning study. [ABSTRACT FROM AUTHOR]

To solve the problem of secondary distribution systems planning, this paper proposes a methodology using a tabu search algorithm as a solution technique. The problem is formulated as a nonlinear mixed-integer model, which takes into account the location and capacity of new elements (distribution transformers and primary-secondary distribution networks), relocation of existing distribution transformers, increasing the capacity of existing elements, secondary network reconfiguration, and phase balance. It also considers the costs associated with connections between primary and secondary networks and energy losses in distribution transformers. The methodology is applied to two test cases: in the first, a comparative analysis with the Chu-Beasley genetic algorithm is made to verify the efficiency of the proposed methodology; and in the second, the results are analyzed in a Colombian distribution system. In both cases, the results are of high quality, supporting the proposed methodology. [ABSTRACT FROM AUTHOR]

Shell and tube heat exchangers are the most important equipment for heat transfer without combustion in plants of chemical processes.There are many methods for designing shell and tube heat exchangers in literature. Among the most known are the Kern's Method, the Method of Bell Delaware, the Method of Tinker, the Method of Wills and Johnston and the Method of Taborek. The objective of this paper is to describe and automate the Taborek's method. It is also realized and optimization of the heat exchanger cost using the genetic algorithm and Simulated Annealing. It can be concluded that the optimization using both methods conduces to similar results, diminishing considerably the optimized exchanger cost. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the variables: makespan time, defective fraction due to operator fatigue and direct labor cost, in a job shop manufacturing system is presented. Two methods are compared; the first one blends elements from VEGA and MOGA algorithms (V&M), while the second one corresponds to a weighted sums method (WSM). Applying these methodologies, it was found that the method V&M has a better performance than the WSM. Comparing he variable makespan time (in hours) the first is better by 7.0 %; comparing the variable makespan time (in weeks) by 6.7 %; and comparing the variable defective fraction by 1.4 %. Regarding the variable labor costs, both methods have the same performance. [ABSTRACT FROM AUTHOR]

In this paper we propose a new methodology for the prediction of nonlinear time series using genetic programming. The proposed approach is based on incorporating the concept of functional blocks and the modification of the genetic algorithm so that it operates with it. The functional blocks represent well known statistical models for the time series forecasting. The proposed algorithm allows the exploration and exploitation of regions where there is greater possibility of finding better forecasting models. Two Benchmark time series were predicted in order to validate the proposed approach, and it was found that our methodology predicts more accurately the time series considered, in comparison with other nonlinear models. [ABSTRACT FROM AUTHOR]

This paper shows an overview of the problem, "School Timetabling", it treats first a definition of this problem, their classification, their computational complexity, and then it makes a review the different techniques which can be solved and the last one review one of these techniques such as genetic algorithms (GA), which was chosen to solution it. [ABSTRACT FROM AUTHOR]

This paper presents a methodology that aims to minimize simultaneously, in a "Jo b Shop" production system the following variables: process time (makespan time), cost of direct labor and also the fraction defective generated by operator fatigue. For this purpose, are taken and fused elements of genetic algorithms Vega and Moga, through the following steps: generating the initial population, form the new population, obtaining the appropriate analysis of variance and finally compared with a hybrid method of weighted sums and genetic algorithms. According to the above, when evaluating the solution faster processing time corresponding to the method based on algorithms Vega and Moga, respect to the solution faster processing time calculated from the method based on weighted sums and genetic algorithms, states that the first one exceeds the second performance as: for process time variable (in hours) at 27.86%, for variable in process time (in weeks) at 1.25%, in terms of the variable cost of direct labor in 6.73% and, as to the variable defective fraction in 25.85%. [ABSTRACT FROM AUTHOR]

This paper presents an initialization technique for a Simple Genetic Algorithm that tunes a Fuzzy Inference System working as a classifier. The proposed technique uses the Fuzzy C-Means (FCM) clustering algorithm to generate the initial population of a Simple Genetic Algorithm. Two classification problems are considered to validate the proposed algorithm and to compare it against a Simple Genetic Algorithm with random initialization. Results show that it is possible to achieve a reduction in generations necessary for finding a desired classifier by using the proposed technique. [ABSTRACT FROM AUTHOR]

This paper describes a solution method to the problem of processing n jobs on m non-related parallel machines. It is a linear and combinatorial generalized allocation problem that considered a sequencedependent setup time and dynamic job entry. A genetic algorithm with integer coding and random generation of population, parent selection, crossover and mutation is proposed. There are two descendants per generation that are compared against the worst existing element to enter to population. After a number of generations that is proportional to the product of nxm the solution is generated. The jobs are sequenced on each machine by due date and computational times are acceptable. It is concluded that the proposed genetic algorithm is an effective and efficient solution that focuses on reducing processing time and on meeting deadlines. [ABSTRACT FROM AUTHOR]

This paper proposes the implementation of an optimum academic agenda to solve the problem of scheduling a school day in Colombia. This is done by taking into account not only the appropriate assignment of rooms and professors but considering the cognitive rhythms of the students as the most relevant factor in the optimization. The Non-dominated Sorting Genetic Algorithm, NSGA-II is proposed as a solution method. The results show a higher efficiency of the algorithm when compared to other algorithms applied to the same problem and evaluated in the same form. At the experimental level the methodology shows a reduction in academic mortality rates in the groups where the methodology was implemented compared to groups that did not considered the cognitive rhythms of the students. [ABSTRACT FROM AUTHOR]

The energy efficiency of an electric system depends in great extent of the reactive power compensation that is carried out. One of the ways most used for reactive is power of capacitor banks. A good selection of these, presupposes three aspects: the selection of the banks capacity, the compensator type to be used and the allocation in the power system. These aspects are treated through independent methods. However, each one depends on the rest and all have an economic implication, which leads to the necessity of their consideration in the optimization problem itself. There are references of methods that consider the three aspects in a same problem, but these present restrictions in their applications for secondary power systems. In the paper, we have developed a method with the use of genetic algorithms, which facilitates a more exact problem formulation. The method has been programmed in software MATLAB, version 7.8 (R2009a), using programming structures of Genetic Algorithm and Direct Search Toolbox. The effectiveness is shown in two study cases of systems hotels, belonging to the commercial building consumer sector. [ABSTRACT FROM AUTHOR]

This paper presents a methodology based on genetic algorithms that allows the reordering of the existing park transformers voltage level I of the distribution systems of Bogotá city, Colombia, in order to maximize the recognition of assets that the regulator CREG (Comisión Reguladora de Energía y Gas) made to the various network operators, as set out in resolution 097 of 2008. For the application of the algorithm, we obtained maximum active power measurements for each hour of the year 2009 in a number of transformers of different capacities, chosen at random, installed in the distribution system CODENSA SA ESP, the company that provides electric service in the Bogotá city. With this information we built representative daily load curves and developed a database that contains the operating costs of moving equipment and tariffs, from which it was possible to model the objective function and constraints of the problem, obtaining a high number of possible combinations (about 1 * 10134) due to the large number of nodes and processors present in the distribution system. Conventional search of a solution in the above situation implies the use of prohibitive times. For this reason we implemented a classical genetic algorithm, thus obtaining an optimal solution that offers a financial gain in the first year, associated with the increase in the usage charge, of $ 253.446.362,47 (COP), profits that could be increased considerably when running the algorithm on larger transformers parks. [ABSTRACT FROM AUTHOR]

In this paper a method to solve the state estimation problem in electric systems applying combinatorial optimization is presented. Its objective is the study of measures with difficult detection errors, which affect the performance and quality of the results when a classic state estimator is used. Due to the mathematical complexity, sensibility indicators are deduced from the theory of leverage points used in the Chu-Beasley optimization algorithm with the purpose of reducing the computational effort and enhance the quality of the results. The proposed method is validated in a 30-node IEEE system. [ABSTRACT FROM AUTHOR]

This paper presents a methodology based on genetic algorithms that allows the reordering of the existing park transformers voltage level I of the distribution systems of Bogotá city, Colombia, in order to maximize the recognition of assets that the regulator CREG ( Comisión Reguladora de Energía y Gas) made to the various network operators, as set out in resolution 097 of 2008. For the application of the algorithm, we obtained maximum active power measurements for each hour of the year 2009 in a number of transformers of different capacities, chosen at random, installed in the distribution system CODENSA SA ESP, the company that provides electric service in the Bogotá city. With this information we built representative daily load curves and developed a database that contains the operating costs of moving equipment and tariffs, from which it was possible to model the objective function and constraints of the problem, obtaining a high number of possible combinations (about 1 * 10134) due to the large number of nodes and processors present in the distribution system. Conventional search of a solution in the above situation implies the use of prohibitive times. For this reason we implemented a classical genetic algorithm, thus obtaining an optimal solution that offers a financial gain in the first year, associated with the increase in the usage charge, of $ 253.446.362,47 (COP), profits that could be increased considerably when running the algorithm on larger transformers parks. [ABSTRACT FROM AUTHOR]

This paper studied the problem of sequencing n jobs in a k-stages flexible flowshop with different number of parallel machines per stage with anticipatory sequence dependent setup times (SDST) and tardiness minimization. The performance of constructive heuristics and the genetic algorithm are compared. The evaluation of the methods is made experimentally over a set of randomly generated test problems. The results indicate that the genetic algorithm do not outperforms all of the compared heuristics. [ABSTRACT FROM AUTHOR]

This paper discusses how two artificial intelligence techniques were combined, neural networks and genetic algorithms for the development of a computational tool used for the estimation of mechanical properties such as tensile strength, uniaxial compressive strength and triaxial compressive strength in sandstones, from petrophysical properties using data from tests of Rock Mechanics Laboratory of the Colombian Petroleum Institute - Ecopetrol SA as training data, to improve the design of non-destructive testing with some degree of confidence and resulting in cost reduction. [ABSTRACT FROM AUTHOR]

In this paper, an asynchronous motor simplified thermal model method for conductances and capacitances estimation is presented. A low invasive technique is used. The developed procedure allows the stator temperature rise prediction, not only for dynamic regimes, but also in case of thermal stability. A parametric estimation is done through a reference model, using a genetic algorithm (GA) as optimizing method. The thermal model parameters are finally obtained with an easer experimental work, than the required by other complex experimental methods or by analytical calculations based on design data. The proposed procedure can be carry out in the particular conditions of industrial environment. Its application is specially useful for asynchronous machine thermal analysis. Using the data of a study case reported in literature, the method validation is done, and is applied in an industrial real case, with good precision resulted from it. [ABSTRACT FROM AUTHOR]

PCA and LDA are two of most widely used techniques for face recognition with IR images. In this paper we report the results obtained by using Genetics Algorithms for optimization the characteristic vector generated by these techniques, by assignation of weights to each vector according its performance in the classification task. It shows that, under the proposed scheme, is able to obtain a lower classification error compared to conventional method. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective analysis including variables of the environment of the energy system which is conditioned by the use of solid fuels, for this reason the analysis includes the external characteristics of the resource (fuel) of residue (ash) and the product (electricity/steam). The energy system used is Termocesar, a Colombian process for generating electricity from carbon. Three functions named thermoeconomic objective function, technology objective function and environmental objective function, are analyzed to establish the best location of the energy The genetic algorithm method is used for the optimization of the system. The results were analyzed for nine combinations of weights and show that for most of the cases the optimum includes coals from the Department of Cesar in Colombia. [ABSTRACT FROM AUTHOR]

The energy efficiency of an electric system depends in great measure of the reactive power compensation that 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, the compensator 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. [ABSTRACT FROM AUTHOR]

In this paper the finite element method is applied to the analysis of a three-phase magnetic induction motor squirrel-cage. Different geometries have been applied in the design of the rotor. These geometric variations of the rotor create an electromagnetic profile for each type of geometry. To study the rotor motion, a frequency analysis of the model has been done. The identifying parameters deduced from the finite element method, are adjusted by evolutionary genetic algorithms. Thus, the parameters in the circuit model were identified for each geometry under consideration. A check of the torque was done to validate the model, verifying the accuracy of the method. [ABSTRACT FROM AUTHOR]

The bullet casings found at crime scenes, are key to start investigations to clarify the facts. A weapon firing a projectile, leaving a set of marks on the cartridges, many of them in his shorts. In this paper we present an algorithm based on a 2D image taken from a bullet casings shorts, use processing techniques and image analysis to obtain some of these characteristic marks left by the weapon, then, based on a database that records the bullet casings found previously in other crime scenes and the K-nn algorithm used to relate the bullet casings with previously registered weapons or with other cartridges found in other scenes. To determine the importance of each characteristic marks, we design a genetic algorithm to further define the optimal number of neighbors. An experiment was conducted with 15 weapons fired 10 or 30 times each obtained promising results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, an efficient approach is proposed for the reduction of the computational effort associated to the solution of the electric power transmission-network expansion planning problem. This approach makes use of a non-linear interior-point method to start the population of a genetic algorithm that iteratively solves the problem of investment. From the beginning of the solution, the generated configurations are of high quality and are located at strategic points of the solution space so that they can evolve towards optimal regions. The investment plan for the genetic algorithm was evaluated on a transmission system through a linear interior-point method. The proposed approach was tested on 6 and 24-bus IEEE systems and on the South Brazilian 46-bus system. An excellent performance of the genetic algorithm was obtained for these systems and a lesser computational effort was required for the solution of the problem. [ABSTRACT FROM AUTHOR]

This paper presents the application of an evolutionary algorithm using Matlab to obtain the optimum main angles of the cutting geometry of a machining tool. The optimization method considered the non linear model of Kaldor and Venuvinod. Different algorithm configurations were tested using data obtained from the literature. Several simulations were carried out to validate the performance of the algorithm and to show the usefulness of the method employed. It is concluded that that the proposed approach shows to be stable and coherent in the results for the cases studied. [ABSTRACT FROM AUTHOR]

Colombian environmental legislation has resorted to an economic instrument called retribution tax -based on the mechanism proposed by Baumol and Oates in their 1973 paper-, to regulate the contamination of bodies of water. This article employs standard genetic algorithms to simulate the behavior of a set of artificial companies that spill contaminants into bodies of water, when they are faced with a uniform tax charge with the same characteristics as the retribution tax. The simulations are aimed at reproducing company behavior in distinct tax application scenarios, for the purpose of evaluating their efficiency and proposing measures aimed at improvement. Such assessment is needed because the contaminating companies have adopted a strategic behavior that Baumol and Oates did not contemplate. It is due to the companies' limited rationality and to the environmental authority's but partial compliance with its invoicing and monitoring activities. The article concludes that, faced with such strategic behavior by the contaminating agent companies, a non-declared higher contamination tax would increase the effectiveness of the control instrument. [ABSTRACT FROM AUTHOR]

In spite of the must efficient operation of a three phase circuit is obtained when the load is fully balanced, from the operative point of view, it is practically impossible to maintain that balance for the 24 hours of the day in all buses of the distribution network. The existence of unbalance in the phase currents produces an increase in power and energy losses in all the primary circuit length, at the same time that the power quality served to customers is decreased by the voltage unbalance in the circuit buses. The presented paper implements a simple genetic algorithm SGA to optimize the phase selection for the distribution transformers and transformers banks connection, as well as two and single phase feeder's connection to the circuit. [ABSTRACT FROM AUTHOR]