In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.