The topic of feature selection has become one of the hottest subjects in machine learning over the last few years. The results of evolutionary algorithm selection have also been promising, along with standard feature selection algorithms. For K-Nearest Neighbor (KNN) classification, this paper presents a hybrid filter-wrapper algorithm based on Equilibrium Optimization (EO). With respect to the selected feature subset, the filter model is based on a composite measure of feature relevance and redundancy. The wrapper model consists of a binary Equilibrium Optimization (BEO). The hybrid algorithm is called filter-based BEO (FBBEO). By combining filters and wrappers, FBBEO achieves a unique combination of efficiency and accuracy. In the experiment, 11 standard datasets from the UCI repository were utilized. Results indicate that the proposed method is effective in improving the classification accuracy and selecting the best optimal features subsets with the least number of features.