Customer Behavior Tracing and Prediction Using Genetic Algorithm: Review of Literature

Prediction of economical situations is one of the most important subjects about making decisions for any country. That’s because it can give a scientific answer about any economical phenomena. It can help the planner to see the future conditions and try to catch what is hidden in economy. In addition to make the best decisions in the company. This can be done by investigating the last tasks and round the returned values in time sequences because it is clear that every time the company updates the numbers of its works shows all the activities the company is making. The planner must stay with contact with these time series and analyze its sequence manner using statistical algorithm, this lids to make a mathematical construction for the idea by building models describes the reality. After the development of the mathematical ways, many non-measurable activities, especially random activities in economical phenomena movement, was a ground to start building random process models, it was reflected on the prediction ways to became a statistical structure model. In recent, after developing of company’s activities and increase of its complexity, new techniques were developed using neural network models that allows us to study non-linear time-series models so it became the most used algorithm in advanced countries. It helps us to create an accurate prediction modeling system. Our search aims to create a tool to predicate the customer behavior in choosing the best manufactures of a company, trying to use genetic algorithm as a ground tool for this decision making and compare our results with previous companies’ activities to evaluate the model created.