Application of Metasynthesis in Identifying New Combined Genetic Algorithm Methods to Solve Problems in Oil Price Forecasting

The current research seeks to identify new combined genetic algorithm methods to solve price forecasting It was oil.Effective factors were identified by using a systematic and meta-composite review approach, and performing the 7 steps of the Sandelowski and Barroso method. Among 4340 articles, 54 articles were selected based on the CASP method. In this context, in order to measure reliability and quality control, the Kappa index was used, and its value was identified for the identified indicators at the level of excellent agreement.The results of the data analysis collected in the ATLAS TI software led to the identification of 7 categories and 26 primary codes of new combined genetic algorithm methods to solve completely difficult problems. Based on the done coding, 7 categories and 26 primary codes were identified. The identified categories are: component design, supply network, planning, forecasting, inventory control, information security, segregation and evaluation.The combination of genetic algorithm with different methods due to its ability to distinguish features and optimize parameters can lead to significant improvements in the field of oil price prediction. The use of genetic algorithm in solving oil price forecasting problems as an evolutionary and artificial intelligence approach makes it possible to integrate diverse and complex information in forecasting models. By designing the appropriate components as genes that represent important economic, geographic and political features, the most optimal genetic combinations can be created to increase the accuracy and performance of prediction models.