Electroencephalograph (EEG) based Braincomputer Interface (BCI) research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Differential Evolution with Temporal Difference Q-Learning (DE-TDQL)-based clustering algorithm to reduce the features and have acquired their corresponding accuracy. Experiments with synthetic and real-world data provide evidence that such an approach leads to improved classification performance. Superiority of the new method is demonstrated by comparing it with three classification methods including Linear Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine-Radial Basis Function. Self-Adaptive Differential Evolution, Differential Evolution/current-to-best/l, Particle Swarm Optimization and Genetic Algorithm-based clustering approaches have also been used here to study the relative performance of the proposed adaptive memetic algorithm-based clustering technique with respect to runtime and classification accuracy.