multi-objective genetic algorithm

Banking is one of the main components of any system and government, and proper management and proper promotion are one of the key factors in the country's economic growth. Banks are exposed to multiple risks as well as lack of control over bank charges; in this regard, appropriate strategies have to be adopted to improve banks' performance in this regard. One of these methods is the selection of partners to divide and reduce risk and share costs, so that they can reduce the Insolvency Risk of Banks and reduce the bank's share of cost control and lead to the bank's growth in financing and ultimately, economic growth in the country. In this research, a multi-objective model for selecting partners in the field of banking has been presented and further optimized using a multi-objective genetic algorithm.In this research, a multi-objective model for selecting partners in the field of banking has been presented and further optimized using a multi-objective genetic algorithm.

Since the improvement of human resource efficiency can play an effective role in the efficiency of organizations, it has always been one of the main research topics.The tendency to leave the organization is one of the factors affecting the efficiency of human capital, which can be predicted using in-data models, conditions governing the organization and the factors affecting it. For this purpose, intelligent algorithms based on neural network and multi-objective genetic algorithm have been used in this research to predict the tendency to leave an organization. In this regard, a standard dataset was created based on a questionnaire of employees satisfaction and the desire to leave the job in the Karoon Oil and Gas Production Company. Then, using an artificial neural network as a classifier and a multi-objective genetic algorithm, the important factors or features were selected. In the next step, an expert system was proposed to select effective features. In order to test and evaluate the neural network algorithm developed with the standard data set, the necessary training was provided. The results showed that by using the proposed model, not only the willingness of employees to leave the organization can be predicted with more than 88% accuracy, but also, the key factors of leaving the organization can be determined.