I have come across some papers discussing the use of genetic algorithms as a forecasting tool. I don't however understand how a genetic algorithm, which to my (limited) knowledge is used to solve optimization problems, can be used for forecasting.
I do realize this is a very general question, but a simplified explanation and suggestions for how to get started with such forecasts would be very helpful and I'm sure of interest to others as well.
Bonde, G., & Khaled, R. (2010). Stock price prediction using genetic algorithms and evolution strategies. Institute of Artificial Intelligence, University of Georgia. Athens.
Best Answer
By reading a little bit the paper you attached, GA is used for feature selection. It seems to me that they are trying to optimize the weights of a logistic regression model in order to predict an increase or decrease of the stock price that is represented as a binary variable. The weights represent the importance of each feature they have chosen empirically to add to their model.They use training and testing datasets from previous years. So they know if their model is predicting well or not. Once they extract the optimal weights using the GA they can apply their model in the present to predict future increases or decreases of a stock price.