genetic algorithms

Increasing the level of safety in the road transport sector has always been one of the most important issues in the transportation system. In the national transport industry, the outsourcing industry always has the highest casualties. By identifying the safety indicators and the points of the extra-urban accident and increasing their level of safety, the percentage of losses and losses can be greatly reduced. Research for modeling human performance has led to the creation of two new research fields, neural networks and fuzzy systems. Neural networks are dynamic systems that, by simulating the functioning of the nervous system and the human brain by processing on experimental data, transfer knowledge or law beyond the data to the network structure, and relying on the ability to learn and ability parallel processing is capable of solving complex problems. Fuzzy systems, based on the approximate human decision-making process, are quantitatively and intuitively modeled quantitative, and thus they are trying to deal with uncertainties. The simplicity and comprehensiveness of this method are its advantages. Neuro-fuzzy systems combine two methods of parallel learning and processing of neural networks and approximate fuzzy inference. In this research, a neuro-fuzzy recurrence network is designed to predict the number of accidents. Then, the genetic algorithm is proposed for a new method in the training of this network and compared to them.

The main purpose of the reliability theory of structural systems is the optimal design based on the reliability concept. Trusses and space structures are widely used, and cost and safety both are important in these structures. Therefore the weight optimization of these structures based on the theory of reliability (probabilistic optimization) using Genetic Algorithms was studied in this paper. The objective function was weight of structure and the constraints were based on the failure probability of elements or nodes or the system. To increase the accuracy of structural safety calculations, the probabilistic form of buckling is presented in this research. The results of this study show that increasing the system allowable failure probability decreases the optimum weight of structure. The process of this weight loss occurred at first with a very fast slope and then with a gentle slope and finally tended to a constant value. Optimum weight was very sensitive to coefficient of variation comparison of allowable failure probability. In the probabilistic optimization, if the system allowable failure probability and coefficient of variation of load and yield stress are considered near zero, and if results are compared to the deterministic optimization, the difference in optimum weight is less than 9%. In this case, convergence for deterministic optimization was very fast and occurred in less than 20 generations but convergence in probabilistic optimization occurred slowly after 85 generations.

Quantum computing is a new method of information processing based on the concepts of quantum mechanics which leads to strange and powerful events in the field of quantum. Each unitary matrix represents a quantum gate. Synthesis of quantum circuits refers to the process of converting a quantum gate into a series of basic gates and is divided into two general categories, namely decomposition and composition-based. In the first category, quantum circuits are synthesized using matrix decomposition methods. This study deals with the second category, which uses evolutionary algorithms and especially genetic algorithms to multiply matrix gates to achieve the final characteristic of a quantum circuit.

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.

In recent years, the capabilities of rotating packed beds in dust removal from gases has attracted the attention in different industries. On the other hand, due to intricate complexities of such beds, employment of computational fluid dynamic techniques for design and modeling purposes would be difficult. Therefore, in this research the performance of artificial intelligence techniques in design of theses equipment was investigated. Accordingly, in the first step and by using 561 experimental data points, an artificial neural network system was developed. In the constructed model, dust removal efficiency was considered as the output and the working and design conditions of the bed were fed as the inputs. Performance of the developed system was analyzed both quantitatively and qualitatively and achieving value of 0.99 for correlation coefficient demonstrates its suitable accuracy in efficiency determination. Finally, employing the developed neural network system combined with genetic algorithms, a novel method for optimum design of beds with arbitrary characteristics was proposed. Performed analyses showed that the constructed system while being simple, offers flexibility and precision in design of rotating packed beds. Also, its low coast of development makes it an attractive idea to be employed in industry scale.

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.

Quantum systems theoretically have a high power in the field of processing and sending information. But their interactions with the outside world lead to the problem of their realization in the real world. Quantum block stabilizer codes have been designed to overcome this problem. Each quantum block code with specific characteristics is described as a binary matrix which is called a generator matrix. Although many studies have been performed on the properties of an error-correcting code with a given generator matrix, a general method has not still been provided for reaching to a generator matrix. In this paper, a method for producing a general generator matrix by using genetic algorithms is presented. Simulation results show the performance of the proposed algorithm.