In [[supervised learning]] models, there are tests that are needed to pass to reduce mistakes. Usually, when mistakes are encountered i.e. test output does not match test input, the algorithms use [[back propagation]] to fix mistakes. Whereas in [[unsupervised learning]] models, the input is classified based on which problems need to be resolved.
For example, Chou<ref>{{cite journal|last=Chou|first=Shi-Hao |author2=Yen-Sen Ni |author3=William T. Lin|title=Forecasting IPO price using GA and ANN simulation|journal=In Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision (ISCGAV'10)|year=2010|pages=145–150|publisher=World Scientific and Engineering Academy and Society (WSEAS)}}</ref> discusses their algorithm for determining the IPO price of [[Baidu]]. They have a three layer algorithm which contains—input level, hidden level, and output level:
* Input level, the data is received unprocessed.
* Hidden level, the data is processed for analyses
* Output level, the data goes through a sigmoid transition function
They reduce the number of errors by trying to find the best route and weight through the neural network which is an evolutionary algorithm.
