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In this paper, a novel harmonic reduction technique in PWM AC voltage controller is proposed to reduce the harmonic contents in current waveform. Our technique is used to evaluate the optimal turn on and turn off angles in PWM such that the total current harmonic distortion is minimized. To accomplish this optimization tasks, in this paper, Genetic Algorithm (GA) is adopted. However, the evolved angles are only optimized at a given desired output voltage. To apply our proposed technique for all of output voltages, artificial neural network (ANN) is investigated to approximate the switching angles from sets of optimal angles evolved by GA. Simulation results show that our proposed technique is suited for designing and gains a better performance and reduction of current harmonics compared to conventional fixed-pulse PWM. [ABSTRACT FROM AUTHOR]

This paper presents a combined model approach of Fuzzy Logic and Genetic Algorithm applied for non-linear behavioral compensation of Silicon Controlled Rectifier (SCR), for its improved performance (optimal variable output voltage). The optimized parametric compensation of SCR will be done by amalgamated algorithm of Fuzzy Logic Control and Genetic Algorithm. It is a shift from existing practice of Fuzzy Logic based control /compensation, as reported in the literature. In this work, a Fuzzy Logic based optimal control system has been developed for input voltage regulation of SCR, which is further optimized by Genetic Algorithm. The input voltage regulation of SCR is needed to meet the varying load current demand in various industrial applications of the device. The proposed scheme as presented in this paper leads to the optimal regulation of input voltage for SCR. The results have shown a remarkable reduction in the error which was otherwise existing in the device and its application circuit. The accuracy level at the output of the SCR after the implementation of the proposed amalgamated algorithm is ranging between 99.0 to 99.5%. It also suits the non-linearly varying load current requirement for a given industrial system employing SCR. [ABSTRACT FROM AUTHOR]

In this paper, multi-objective genetic algorithm (MOGA) is proposed to combine the advantages of fixed-structure robust H∞ control and the model reference control. Robust controller designed by the conventional H∞ optimal control is complicated and high order. This problem can be solved by using a genetic algorithm to search the optimal parameters in a specified structure robust controller. The fitness function for this technique is based on the concept of robust control, which normally is specified in frequency domain. However, in many cases, time domain specifications such as overshoot, undershoot, rise time are also important. To enhance the GA based robust control technique, in this paper, the cost function of model reference control is added into the GA process to incorporate the time domain specification. By the proposed approach, the robustness, frequency domain and time domain specifications can be achieved simultaneously. Simulation results in a servo system show the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

A standard genetic algorithm is asexual. To mimic nature more closely, this paper incorporates gender in a genetic algorithm. Two individuals from opposite sex are permitted for reproduction. Earlier researchers considered that the gender of the offspring depends on the gender of the individual removed from the population. Thus the number of males and females are kept constant. This assumption was relaxed by introducing randomness during gender assignment to the offspring after the crossover operation. However, due to the stochastic nature of the process, this may generate a population of single sex which leads to no regeneration being possible. This paper introduces adaptive gender assignment to the offspring so that the gender of the offspring is decided based on the gender density in the population. The proposed algorithm is applied to design a pressure vessel. [ABSTRACT FROM AUTHOR]

A quality selection problem generally identifies the process parameters such as the initial process means of a production process. The joint determination of the initial process means and the production run has been reported in the literature. Most of these studies considered a process with multiple independent nominal-the-better type of quality characteristics. In a real industrial situation, the quality characteristics may depend on each other. In addition, the process could have other types of quality characteristics. In this paper, a mathematical model is developed that jointly determines the optimum initial process means and production run of a process with multiple dependent smaller-the-better type of quality characteristics. The genetic algorithm, a global optimisation algorithm, is used as an optimisation algorithm in this study, whereas local optimisation algorithms have commonly been used in preceding studies. [ABSTRACT FROM AUTHOR]

Determining the relevancy of web images for a query term is basic to any image search engine. We have designed and developed a search method that deployed image retrieval and optimization techniques to provide a more significant and restricted set of images as the final result for a user's search on an image metasearch engine. A vectorization method was adopted to apply PCA (Possibilistic Clustering Algorithms) clustering techniques on images search that are then refined by a specially designed genetic algorithm. The method, its implementation and the algorithms applied were described in the paper. Some experiments have been run on to test the image metasearch engine. The method enables the image metasearch engine to handle a query term in a reasonably short time and return the results with high accuracy. [ABSTRACT FROM AUTHOR]

Web services are being adopted, more and more, as a viable means of accessing Web-based applications. Web services are not only used to exchange information between enterprises but also help software developers to provide value-add services for various demands. At present, there is a trend towards deploying business processes as composite Web services, known as Web services compositions. Web services compositions are synthesized by researchers from elementary web services, offering the opportunity for service providers and application developers to create value-added services, through Web services composition. However, a problem exists in the current distribution process of Web services compositions: the general analysis and selection of services can be overly complex and un-systemic, for example, information in Universal Description, Discovery and Integration (UDDI) are missed, uncompleted or inaccurate. Therefore, there is a need to manage composite Web services, based on these emerging technologies. But the research related to ranking candidate services and selection of optimization strategies is sparse. Nothing has yet been published, that considers the constraints of non-functional properties. In this paper, a systemic but complete composition process has been proposed, which shows great promise in the emerging demand for composite Web services. Also by proposing the new constrained crossover and constrained mutation process, the Genetic Algorithm (GA) can operate effectively thus to produce an optimal solution (the best combination of Web services). [ABSTRACT FROM AUTHOR]

Genetic algorithm(GA) is an important technology of data mining, it gets successful application in many fields. In order to raise efficiency of genetic algorithm, we need to improve its basic factors. This paper researches basic factors and improvement technology of GA, proposes some improvement approaches of crossover operator and mutation operator, and makes proving for improved genetic algorithm. It has proved that the advantage of improved genetic algorithm is obvious. [ABSTRACT FROM AUTHOR]

Hardware-software partitioning is a critical issue while developing embedded multiprocessor FPGA systems. In this paper, we propose an efficiently hardware-software partitioning method, namely GHO, that profits the advantages of genetic algorithm and hardware oriented partition to solve the partitioning issues for embedded multiprocessor FPGA systems. In addition, GHO can gain lead to a better partitioning result, faster execution time and lower memory used rate, under satisfied system constraints. Finally, a JPEG encoding system example is used to demonstrate the feasibility for our proposed GHO. Experiment results show that the execution time and memory size are improved by GHO in Xilinx ML310 FPGA platform. [ABSTRACT FROM AUTHOR]

Gene expression profiles have great potential as a medical diagnosis tool, since they represent the state of a cell at the molecular level. In the classification of cancer type research, available training data sets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed in order to improve predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this paper, we propose to combine genetic algorithms (GAs) and improved binary particle swarm optimization (IBPSO) for feature selection. PSO serves as a local optimizer each time the GA has been run for a single generation. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as an evaluator of the GA and IBPSO. The proposed method is applied and compared to six classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods. [ABSTRACT FROM AUTHOR]

As malicious intrusions are a growing problem, we need a solution to detect the intrusions accurately. Network administrators are continuously looking for new ways to protect their resources from harm, both internally and externally. Intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. The objective of this paper is to describe a fuzzy c-means and genetic algorithms and discuss its usage to detect intrusion in a computer network. We are using a Genetic Algorithms (GA) to select a subset of input features for clustering with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Experiments were performed with DARPA data sets, which have information on computer networks, during normal behavior and intrusive behavior. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mechanism for filtering e-mails by constructing spam mail prototypes by using genetic algorithm. Keywords extracted from e-mail's subject and body are categorized by their meaning into 8 categories. The binary representation of a chromosome string having 8 genes is constructed from keywords. Genetic operations are applied to create varieties of spam mail prototypes for filtering. From the experiments, the proposed technique shows the accuracy of filtering spam mails by 87.05% in average. When comparing with the other technique liked Bayesian, the proposed technique still has higher accuracy. [ABSTRACT FROM AUTHOR]

The goal of resource leveling is to minimize the deviation between the resource requirements and the desired resource profile to prevent problems that caused by fluctuation of required resources. It is a common problem and arises frequently in real situations and therefore has been studied numerous times. Almost in all of these studies and the resulting solutions, there exist a common element, which is once an activity is started, it cannot be stopped and restarted again. That is, it cannot be split. In many instances in actual construction, there exist activities that can be split. So, it seems to be very useful to develop a model for resource leveling problems that allows certain activities to be split. An important interesting property that such a model should have is its simplicity. By simplicity we mean its ease of use by inexpert users without strong mathematical background. This paper presents a model based on genetic algorithms to level resources that permits selected activities to stop and restart. This splitting of activities results in improvement to the leveling solution that is traditionally achieved when splitting is not allowed. Examples are presented that illustrate the improvement in the solution obtained from the proposed model compared to models that do not allow splitting and compares the result to that obtained using commercially available software. The model presented here is very efficient and effective. Therefore, it can be used in real situations to automatically solve resource leveling problems efficiently to obtain effective results. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a stochastic and population-based search algorithm that demonstrates its effctiveness in solving complex nonlinear optimization problems. Although the original PSO is very simple and effective, how to determine appropriate values of parameters in PSO is yet to be found. This paper proposes a novel method called evolutionary PSO, which estimates values of parameters in PSO for effectively finding globally optimal parameter values by a real-coded genetic algorithm. A crucial idea here is to adopt a temporary cumulative fitness instead of instantaneous fitness in a real-coded genetic algorithm for evaluating the performance of the PSO. It provides a useful measure that efficiently determines appropriate values of parameters in PSO. To demonstrate the effectiveness of the proposed method, we implement a simple computer experiment on a 2-dimensional optimization problem, and analyze the characteristics of dependency on initial condition. [ABSTRACT FROM AUTHOR]

The problem of parts nesting in plane areas is found in several industries, with restrictions and different objectives. Among these industries there are packings and clothing industries, which use irregular parts as concave and convex ones. A peculiarity of the packing industry is the frequent use of a single part type for each nesting process. This paper describes genetic algorithms (GA) and heuristic rules to solve the nesting problem. The results are also presented and discussed. [ABSTRACT FROM AUTHOR]

The development of data mining systems has received a great deal of attention in recent years. It plays a key enabling role for competitive businesses in the E-commerce era. One of the challenges in developing data mining systems is to integrate and coordinate existing data mining applications in a seamless manner so that cost-effective systems can be developed without the need of costly proprietary products. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(Association Rules Mining in Novel Genetic Algorithm), Compared to the algorithm in Reference [2] , the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time. [ABSTRACT FROM AUTHOR]