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In recent years, piecewise linearization has developed as an attractive tool for the representation of various complex nonlinear systems. The piecewise linearization of individual functions provide a platform for the piecewise affine approximation of nonlinear systems containing a large number of scaler valued nonlinear functions. Inspite of the wide application of piecewise linearization, the optimal...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
Scheduling heterogeneous tasks in a heterogeneous grid environment aims at effectively utilizing the resources and sharing the load among the available resources. Such a task assignment problem is NP-hard. This paper presents a Hybrid Particle Swarm Optimization - Genetic Algorithm (HPSO-GA) for solving the Task Assignment Problem. The novel Particle Swarm Optimization (PSO) implements GA operations...
Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
Cluster analysis is used in several research areas to classify data sets in groups by their similar characteristics. Metaheuristic-based techniques, such as Genetic Algorithms (GAs) and Ant Colony Optimization (ACO), have been applied in order to increase the clustering algorithm performance. GA and ACO-based clustering algorithms are capable of efficiently and automatically forming natural groups...
This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image...
The biclustering problem consists in simultaneously clustering rows and columns of a data matrix. The aim of this paper is to empirically assess the performance of cooperative coevolution as an alternative approach for coping with the task of discovering good and sizeable biclusters. For this purpose, two cooperative coevolutionary algorithms, one configured with genetic algorithms (GAs) and another...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and new customer personalization is one of these problems. In this paper, a method combining GA based clustering algorithm with Collaborative Filtering CF-based Recommender system is proposed named Information Gain Clustering using Genetic Algorithm (IGCGA), which alleviates the problem of...
This paper proposes a new approach for expansion planning of Subtransmission System (SS). Distribution network (downward grid) is considered in the problem by modeling it as the load points, then Modified Mathematical Clustering Algorithm (MMCA) has been used for candidate selection of subtransmission substations. Finally Genetic Algorithm (GA) is employed to allocate the load points to the existing...
An attempt has been made in the paper to find globally optimal cluster centers for remote-sensed images with the proposed Rapid Genetic k-Means algorithm. The idea is to avoid the expensive crossover or fitness to produce valid clusters in pure GA and to improve the convergence time. The drawback of using pure GA in the problem is the usage of an expensive crossover or fitness to produce valid clusters...
Intrusion detection systems (IDS) usually trigger a great number of alarm messages that frequently overwhelm their human operators. Hierarchically clustering technique is able to help IDS operators to get meaningful overviews from the great number of alarms. A dilemma is encountered when the clusters are generated. If the clusters are obtained one by one, they cannot be prevented from overlapping...
The Game Theory-based Multi-Agent System (GTMAS) of Salhi and Töreyen, and implements a loosely coupled hybrid algorithm that may involve any number of algorithms suitable, a priori, for the solution of a given optimisation problem. The system allows the available algorithms to cooperate toward the solution of the problem in hand as well as compete for the computing facilities they require to run...
In this analysis a process to demarcate areas with analogous wind conditions is shown. For this purpose a dispersion graph between the wind directions will be traced for all stations placed in the studied zone. These distributions will be compared among themselves using the centroids extracted with SOFM algorithm. This information will be used to build a matrix, allowing us working simultaneously...
Radial basis function Networks (RBFNs) have been successfully employed in different Machine Learning problems. The use of different radial basis functions in RBFN has been reported in the literature. Here, we discuss the use of the q-Gaussian function as a radial basis function employed in RBFNs. An interesting property of the q-Gaussian function is that it can continuously and smoothly reproduce...
A novel algorithm, the k-means clustering algorithm based on immune genetic algorithm (KMCIGA) is put forward. To improve the Genetic operators, the conception of concentration in the immune algorithm and the dynamic chromosome coding are used. Strategies and methods of selecting vaccines and constructing an immune operator are also given. KMCIGA is illustrated to be obviously better than the traditional...
This paper presents Genetic Algorithm based sentence extraction strategy and threshold based document clustering algorithm to produce cluster wise optimal summary. Related documents are grouped into same cluster using threshold based document clustering algorithm. From each cluster important sentences are selected using feature profile which is generated by considering sentence specific features like...
This work is concerned with customer-oriented catalog segmentation that each catalog consists of specific number of products. In this problem, requirements of a specific ratio of customers should be satisfied. According to the definition, when a customer is satisfied that at least t required products exist in his/her catalog. The objective of this problem is to minimize the number of catalogs, regarding...
Automatic data clustering through determination of optimal number of clusters from the data content, is a challenging proposition. Lack of knowledge regarding the underlying data distribution poses constraints in proper determination of the inherent number of clusters. A differential evolution (DE) algorithm based approach for the determination of the optimal number of clusters from the data under...
Aiming to the shortages of fuzzy c-means clustering applied to pattern recognition, an improved method by genetic algorithm is proposed. This method can not only automatically optimizes the classification number, but also search the global optimal solution for the clustering center. The experimental results demonstrate this proposed method is excellent for pattern recognition.
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