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Various electromagnetic (EM) structures become more complex and often have increasing degrees of design freedom. Classical optimization methods require numerous simulation trials of different parameter combinations, which leads to a low design efficiency. To address this problem, an efficient EM structure optimization algorithm which combines differential evolution (DE) with machine learning technology...
Stock market is a very challenging and an interesting field. In this paper, we are trying to predict the target prices of the stocks for the short term. We are predicting the target priceof script individually for eight different scripts. For each script, six attributes are used which help us to find, whether the prices are going up or down. The evolutionary techniques used for this experiment are...
Persons are often asked to provide information about themselves. These data are very heterogeneous and result in as many “profiles” as contexts. Sorting a large amount of profiles from different contexts and assigning them back to a specific individual is quite a difficult problem. Semantic processing and machine learning are key tools to achieve this goal. This paper describes a framework to address...
Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. However there are still many areas in which humans excel in comparison with the machines. One such area is chess. Even with great advances in the speed and computational power of modern machines, Grandmasters...
The presence of a large number of irrelevant features degrades the classifier accuracy, reduces the understanding of data, and increases the overall time needed for training and classification. Hence, Feature selection is a critical step in the machine learning process. The role of feature selection is to select a subset of size ‘d’ (d<n) from the given set of ‘n’ features that leads to the smallest...
Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multi-population genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely...
The aim of the paper is to report a new method based on genetic computation of designing a nonlinear soft margin SVM yielding to significant improvements in discriminating between two classes. The design of the SVM is performed in a supervised way, in general the samples coming from the classes being nonlinearly separable. The experimental analysis was performed on artificially generated data as well...
Cognitive radio is an intelligent radio that has the ability to sense and learn from its environment. Basic core of cognitive radio contains a learning engine and it plays an important role in every application of cognitive radio from spectrum sensing to spectrum management. Learning engine implements different learning algorithms. In this paper we discuss various learning algorithms and their application...
Machine learning have been one of the most considered techniques for achieving automatic intrusion detection. Despite many of these machine learning approaches have achieved the goal of getting high accuracy levels in a more automatic way, the fact is that only a few of them have actually been deployed on real life scenarios. This could be explained if we take into consideration that some of the assumptions...
Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and metaheuristic algorithms that were tailored to deal with scheduling of independent jobs. In this study we investigate the efficiency of three bio-inspired metaheuristics for finding good schedules...
Association rules are import basis of describing Web users' behavior characteristic. Traditional algorithms of Web association rules mining, based on statistics, usually pays attention to the analysis on existing data,they can't offer effective predictive means and optimizing measure and can not find out the latent and possible rules. This paper presents a kind of system of the Web association rules...
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