The wrapper model popularized by Kohavi as mentioned @Peter would help in finding optimal features which are not necessarily relevant to your target labels. In the same paper, Kohavi states that "relevance does not imply optimal" and vice versa. In addition, the generally defined wrapper model does not rank the importance of your features.
You may follow one of the following solutions to rank the features selected by the wrapper model:
1- Rank the featuers using some filtering method as mRMR. Then, using forward selection you optimize your classifier and once the performance degrades you stop.
2- Select the featuers by the wrapper model and then, rank the selected ones by mRMR (opposite of 1).
3- Run the wrapper model over different folds of your data and then, you rank selected featuers based on frequency of being selected in different folds. An ensemble idea can follow this framework for featuer selection, also.