Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A hybrid linear-neural model for time series forecasting
IEEE Transactions on Neural Networks
Dealings with problem hardness in genetic algorithms
WSEAS Transactions on Computers
Applications of neural networks in continuous casting
WSEAS TRANSACTIONS on SYSTEMS
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Selection of effective input variables on decision making or forecasting problems, is one of the most important dilemmas in forecasting and decision making field. Due to research and problem constraints, we can not use all of known variables for forecasting or decision making in real world applications. Thus, in decision making problems or system simulations, we are trying to select important and effective variables as good data. In this paper we use a hybrid model of Genetic Algorithm (GA) and Artificial Neural Network (ANN) to determine and select effective variables on forecasting and decision making process. In this model we have used genetic algorithm to code the combination of effective variables and neural network as a fitness function of genetic algorithm. The introduced model is applied in a case study to determine effective variables on forecasting future dividend of the firms that are members of Tehran stock exchange. This model can be used in different fields such as financial forecasting, market variables prediction, intelligent robots decision making, DSS structures, etc.