multi-layer perceptron neural network training based on improved of stud ga

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hence, in this paper, it is attempted to use improve stud ga to find optimal weights for multi-layer perceptron neural network. stud ga is improved from genetic algorithms that perform information sharing in a particular way.  in this study, chaotic system will be used to improve stud ga. the comparison of proposed method with imperialist competitive algorithm, quad countries algorithm, stud ga, cuckoo optimization algorithm and chaotic cuckoo optimization algorithm on tested data set (wine, abalone, iris, wdbc, pima and glass) with determined parameters, as mentioned the proposed method has a better performance.