H.3.15.3. Evolutionary computing and genetic algorithms
Mahdieh Maazalahi; Soodeh Hosseini
Abstract
Detecting and preventing malware infections in systems is become a critical necessity. This paper presents a hybrid method for malware detection, utilizing data mining algorithms such as simulated annealing (SA), support vector machine (SVM), genetic algorithm (GA), and K-means. The proposed method combines ...
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Detecting and preventing malware infections in systems is become a critical necessity. This paper presents a hybrid method for malware detection, utilizing data mining algorithms such as simulated annealing (SA), support vector machine (SVM), genetic algorithm (GA), and K-means. The proposed method combines these algorithms to achieve effective malware detection. Initially, the SA-SVM method is employed for feature selection, where the SVM algorithm identifies the best features, and the SA algorithm calculates the SVM parameters. Subsequently, the GA-K-means method is utilized to identify attacks. The GA algorithm selects the best chromosome for cluster centers, and the K-means algorithm has applied to identify malware. To evaluate the performance of the proposed method, two datasets, Andro-Autopsy and CICMalDroid 2020, have been utilized. The evaluation results demonstrate that the proposed method achieves high true positive rates (0.964, 0.985), true negative rates (0.985, 0.989), low false negative rates (0.036, 0.015), and false positive rates (0.022, 0.043). This indicates that the method effectively detects malware while reasonably minimizing false identifications.
H.3.15.3. Evolutionary computing and genetic algorithms
A.M Esmilizaini; A.M Latif; Gh. Barid Loghmani
Abstract
Image zooming is one of the current issues of image processing where maintaining the quality and structure of the zoomed image is important. To zoom an image, it is necessary that the extra pixels be placed in the data of the image. Adding the data to the image must be consistent with the texture in ...
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Image zooming is one of the current issues of image processing where maintaining the quality and structure of the zoomed image is important. To zoom an image, it is necessary that the extra pixels be placed in the data of the image. Adding the data to the image must be consistent with the texture in the image and not to create artificial blocks. In this study, the required pixels are estimated by using radial basis functions and calculating the shape parameter c with genetic algorithm. Then, all the estimated pixels are revised based on the sub-algorithm of edge correction. The proposed method is a non-linear method that preserves the edges and minimizes the blur and block artifacts of the zoomed image. The proposed method is evaluated on several images to calculate the optimum shape parameter of radial basis functions. Numerical results are presented by using PSNR and SSIM fidelity measures on different images and are compared to some other methods. The average PSNR of the original image and image zooming is 33.16 which shows that image zooming by factor 2 is similar to the original image, emphasizing that the proposed method has an efficient performance.
H.3.15.3. Evolutionary computing and genetic algorithms
H.R Keshavarz; M. Saniee Abadeh
Abstract
In Web 2.0, people are free to share their experiences, views, and opinions. One of the problems that arises in web 2.0 is the sentiment analysis of texts produced by users in outlets such as Twitter. One of main the tasks of sentiment analysis is subjectivity classification. Our aim is to classify the ...
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In Web 2.0, people are free to share their experiences, views, and opinions. One of the problems that arises in web 2.0 is the sentiment analysis of texts produced by users in outlets such as Twitter. One of main the tasks of sentiment analysis is subjectivity classification. Our aim is to classify the subjectivity of Tweets. To this end, we create subjectivity lexicons in which the words into objective and subjective words. To create these lexicons, we make use of three metaheuristic methods. We extract two meta-level features, which show the count of objective and subjective words in tweets according to the lexicons. We then classify the tweets based on these two features. Our method outperforms the baselines in terms of accuracy and f-measure. In the three metaheuristics, it is observed that genetic algorithm performs better than simulated annealing and asexual reproduction optimization, and it also outperforms all the baselines in terms of accuracy in two of the three assessed datasets. The created lexicons also give insight about the objectivity and subjectivity of words.
H.3.15.3. Evolutionary computing and genetic algorithms
M. B. Dowlatshahi; V. Derhami
Abstract
A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP ...
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A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP is known as an NP-hard problem with practical applications like electronic commerce, production management, games theory, and resources allocation in multi-agent systems. This has motivated the quest for efficient approximate algorithms both in terms of solution quality and computational time. This paper proposes a hybrid Ant Colony Optimization with a novel Multi-Neighborhood Local Search (ACO-MNLS) algorithm for solving Winner Determination Problem (WDP) in combinatorial auctions. Our proposed MNLS algorithm uses the fact that using various neighborhoods in local search can generate different local optima for WDP and that the global optima of WDP is a local optima for a given its neighborhood. Therefore, proposed MNLS algorithm simultaneously explores a set of three different neighborhoods to get different local optima and to escape from local optima. The comparisons between ACO-MNLS, Genetic Algorithm (GA), Memetic Algorithm (MA), Stochastic Local Search (SLS), and Tabu Search (TS) on various benchmark problems confirm the efficiency of ACO-MNLS in the terms of solution quality and computational time.
H.3.15.3. Evolutionary computing and genetic algorithms
Sh. Lotfi; F. Karimi
Abstract
In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving ...
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In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving multi-objective optimization problems (MOPs) with two or three conflicting objectives. This improved hybrid algorithm, namely MOEA/D-TS, uses the parallel computing capacity of MOEA/D along with the neighborhood search authority of TS for discovering Pareto optimal solutions. Our goal is exploiting the advantages of evolutionary algorithms and TS to achieve an integrated method to cover the totality of the Pareto front by uniformly distributed solutions. In order to evaluate the capabilities of the proposed method, its performance, based on the various metrics, is compared with SPEA, COMOEATS and SPEA2TS on well-known Zitzler-Deb-Thiele’s ZDT test suite and DTLZ test functions with separable objective functions. According to the experimental results, the proposed method could significantly outperform previous algorithms and produce fully satisfactory results.
H.3.15.3. Evolutionary computing and genetic algorithms
V. Majidnezhad
Abstract
In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of ...
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In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neighbours) and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on Russian Language. The experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. While the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on Russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%).
