genetic algorithms

Not all phishing attacks are always done in the form of website forgery and telephone phishing. Emails and messages that appear to be sent by the bank and receive information from the user can also be a phishing attack. Feature selection and sample selection are two very important issues in the data processing stage in detecting malicious emails. In particular, identifying spam without data reduction will not be nearly as accurate in the results. Most articles and research have focused on one of these issues, and there are few articles that have worked in combination to detect malicious emails. Therefore, the purpose of the present study is to provide a method to reduce the data in identifying emails by selecting features and samples simultaneously. In the proposed method in this paper, the forbidden search algorithm and the genetic algorithm are used in combination and simultaneously. For the suitability of this method, the evaluation vector machine evaluation function was used. The results showed that the detection rate of spam and e-mails in LineSpam and UCI datasets was 97.28, which was the highest possible value compared to other algorithms proposed in previous studies.

One of the important aspect of cloud computing is processing of amount of big data. MapReduce has been widely used as a powerful data processing model. It has efficiently solved a wide range of large-scale computing problems. MapReduce is a vital programming model for large-scale data processing in the cloud computing, which simplifies the development of traditional distributed program and provides a simple parallel programming method. On the other hand, Task scheduling is critical, which is an NP-completeness problem, plays a critical key role in cloud computing systems. In this paper, we propose a parallel genetic based algorithm to schedule the task on heterogeneous cloud environments. We prompt the algorithm on heterogeneous systems, where resources are of computational and communication heterogeneity. During the implementation of our method, we use Hadoop platform as the backend MapReduce engine. At the last part, through a series of simulation experiments, we prove that our approach has a much better runtime performance than other approach. The main goal of the proposed method is to use MapReduce framework to reduce the overall execution time of the program. The results of tests on a series of directional dag with random input indicate that the proposed method compare with three other existing method in this proposed method the speed of convergence is improved.

In this study, a new method is presented for offline signature recognition. To compare the features, a new similarity measure is introduced based on the number of matched features. In addition, in the post-processing step, an innovative, efficient and effective coordinate matching filter is used that has low computational cost and is consistent with the feature extraction algorithm. This filter applies a threshold on Cartesian coordinate difference between the two blocks on the corresponding images. The implementation of the proposed system includes optimized features that are invariant to scaling and rotation changes. Using the new similarity criteria for matching these features, and post-processing routine using the proposed coordinates filter, applied on the GPDS960 (Offline) and SVC2004 (online converted to online) database, improved efficiency of the proposed identification system. Also proposed system parameters are selected and personalized automatically only once by using a genetic algorithm for each database.

Hyper spectral remote sensing imagery، due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional space، they are efficient tool for classification of hyper spectral imagery. However، there are two optimization issues which strongly effect on the SVMs performance: Optimum SVMs parameters determination and optimum feature subset selection. Traditional optimization algorithms are appropriate in limited search space but they usually trap in local optimum in high dimensional space، therefore it is inevitable to apply meta-heuristic optimization algorithms such as Genetic Algorithm to obtain global optimum solution. This paper evaluates the potential of different proposed optimization scenarios in determining of SVMs parameters and feature subset selection based on Genetic Algorithm (GA). Obtained results on AVIRIS Hyper spectral imagery demonstrate superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset. In Gaussian and Polynomial kernels، the classification accuracy improves by about 5% and15% respectively and more than 90 redundant bands are eliminated. For comparison، the evaluation is also performed by applying it to Simulated Annealing (SA) that shows a better performance of Genetic Algorithm especially in complex search space where parameter determination and feature selection are solve simultaneously.

Designing timetables, for example course timetables in an institute, is one of the most complicated and time-consuming challenges for personnel. Automating it, not only can help the personnel to manage their work better, but also can be considered as a desired sample to assess the ways of planning and to tackle the constraint satisfaction in artificial intelligence. In this paper, genetic algorithms are primarily studied and then it is applied for optimization of an imaginary faculty course timetable. The new designed algorithm is based on keeping the better chromosomes of the population and employing genetic operators on the others in order to improve the overall quality of genes. Some other amendments are also carried out to develop a more capable genetic algorithm for TT applications, compared to the standard one. According to the tests, the new GA algorithm will be more successful in generating high fidelity TTs which do not break any hard constraint. The proposed ideas, in this approach are applicable in other similar situations.