I have a question about Boosting and stacking in machine learning. Suppose that I will train neural network, SVM and logistic regression using optimization algorithm to optimize best inputs in first phase. So now I have 3 trained models with different combination of inputs. Can I use these trained models (based on different input's variables of each created model) for boosting or stacking?
I am not sure if you are familiar with this topic. If not, read Ensemble learning on Wikipedia.
Now, to your question. First boosting. Here, you use a meta-algorithm to improve an (one) algorithm. It usually does a good job, but sometimes it over-fits, meaning that It appears to be better, but it actually isn't. For your problem - yes, you can use boosting on each of the used algorithms.
As for stacking - here, you run all of the algorithms at once, and then make a decision which of them you will use for the final decision (somewhat similar to voting). You usually make this decision with logistic regression (in your case, you will have two different instances of logistic regression as you already use one for solving your problem).
If you want to use both boosting and stacking, you first have to boost each and every one of your algorithms, and then use another algorithm (e.g. logistic regression) to stack them into an ensemble.