List of Articles Genetic algorithm

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  1 - Applying genetic algorithm for automatic service identification based on quality metrics
  Jan Mohammad Rajabi saeed parsa masoud bagheri ali akbar
  Service-oriented architecture improves the stability and operational capability of software systems for passive defense measures. Automatic identification of services using quality of service measures ensures the successful deployment of service-oriented architecture an More

  Service-oriented architecture improves the stability and operational capability of software systems for passive defense measures. Automatic identification of services using quality of service measures ensures the successful deployment of service-oriented architecture and is great importance to speed up software development life cycle. Little attention to non-functional requirements, no considerations for concurrent effects of business activities and entities and non-automated ranking of candidate services are the major issues with current approaches. The approach proposed in this paper considers both the business processes and entities, simultaneously to detect services. Applying a genetic algorithm, candidate services are identified based on quality metrics i.e. granularity, coupling, cohesion and convergence. These metrics are obtained from breaking goals to requirements of level. The TOPSIS method is applied to rank the candidate services. The illustrated case study is shown that high quality services can be identified automatically with minimal software developer’s interventions. Manuscript profile
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  2 - Determination of Optimum SVMs Based on Genetic Algorithm in Classification of Hyper spectral Imagery
  farhad samadzadegan hadise hassani
  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 More

  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. Manuscript profile
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  3 - Using a Hybrid PSO-GA Method for Capacitor Placement in Distribution Systems
  mohammadmahdi Varahram amir mohammadi
  20.1001.1.27170411.1393.6.20.1.9
  In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but ar More

  In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to the initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. This new algorithm can perform faster and does not depend on initial population and can find optimal solutions with acceptable accuracy. Optimal capacitor placement and sizing have been found using this hybrid PSO-GA algorithm. We have also found the optimal place and size of capacitors using GA and PSO separately and compared the results. Manuscript profile
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  4 - Scheduling tasks in cloud environments using mapping framework - reduction and genetic algorithm
  nima khezr nima jafari novimipour
  20.1001.1.27170414.1397.10.37.2.7
  Task scheduling is a vital component of any distributed system such as grids, clouds, and peer-to-peer networks that refer tasks to appropriate resources for execution. Common scheduling methods have disadvantages such as high time complexity, inconsistent execution of More

  Task scheduling is a vital component of any distributed system such as grids, clouds, and peer-to-peer networks that refer tasks to appropriate resources for execution. Common scheduling methods have disadvantages such as high time complexity, inconsistent execution of input tasks, and increased program execution time. Exploration-based scheduling algorithms to prioritize tasks from Manuscript profile
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  5 - A Task Mapping and Scheduling Algorithm based on Genetic Algorithm for Embedded System Design
  mohadese nikseresht Mohsen Raji
  20.1001.1.27170414.1400.13.47.12.8
  Embedded system designers face numerous design requirements and objectives (such as runtime, power consumption and reliability). Since meeting one of these requirements mostly contradicts other design requirements, it seem to be inevitable to apply multi-objective appr More

  Embedded system designers face numerous design requirements and objectives (such as runtime, power consumption and reliability). Since meeting one of these requirements mostly contradicts other design requirements, it seem to be inevitable to apply multi-objective approaches in various stages of designing embedded systems, including task scheduling step. In this paper, a multi-objective task mapping and scheduling in the design stage of the embedded system is presented. In this method, tasks are represented by task graphs assuming that the hardware architecture platform is given and determined. In order to manage the dependencies between tasks in the task graph, a segmentation method is used, in which the tasks that can be executed simultaneously are specified in a segment and is considered in the scheduling process. In the proposed method, the task mapping and scheduling problem is modeled as a genetic algorithm-based multi-objective optimization problem considering execution time, energy consumption, and reliability. In comparison to similar previous works, the proposed scheduling method respectively provides 21.4%, 19.2%, and 20% improvement in execution time, energy consumption, and reliability. Applying a multi-objective helps the designer to pick out the best outcome according to different considerations. Manuscript profile
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  6 - Presenting the model for opinion mining at the document feature level for hotel users' reviews
  ELHAM KHALAJJ shahriyar mohammadi
  20.1001.1.27170411.1400.13.49.6.1
  Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the More

  Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the field of tourism and hotel industry, there are huge and rich sources of ideas in the form of text that people can use text mining methods to discover the opinions of. Due to the importance of user's sentiments and opinions in the industry, especially in the tourism and hotel industry, the topics of opinion research and analysis of emotions and exploration of texts written by users have been considered by those in charge. In this research, a new and combined method based on a common approach in sentiment analysis, the use of words to produce characteristics for classifying reviews is presented. Thus, the development of two methods of vocabulary construction, one using statistical methods and the other using genetic algorithm is presented. The above words are combined with the Vocabulary of public feeling and standard Liu Bing classification of prominent words to increase the accuracy of classification Manuscript profile