Algorithms for automatic model selection

I would like to implement an algorithm for automatic model selection. I am thinking of doing stepwise regression but anything will do (it has to be based on linear regressions though). My problem is that I am unable to find a methodology, or an open source implementation (I am woking in java). The methodology I have in mind would be something like: calculate the correlation matrix of all the factors pick the factors that have a low correlation to each other remove the factors that have a low t-stat add other factors (still based on the low correlation factor found in 2.). reiterate several times until some criterion (e.g AIC) is over a certain threshold or cannot or we can't find a larger value. I realize there is an R implementation for this (stepAIC), but I find the code quite hard to understand. Also I have not been able to find articles describing the stepwise regression.