Extreme Learning Machine (ELM) is an elegant technique for training Single-hidden Layer Feedforward Networks (SLFNs) with extremely fast speed that attracts significant interest recently. One potential weakness of ELM is the random generation of the input weights and hidden biases, which may deteriorate the classification accuracy. In this paper, we propose a new Memetic Algorithm (MA) based Extreme Learning Machine (M-ELM) for classification problems. M-ELM uses Memetic Algorithm which is a combination of population-based global optimization technique and individual-based local heuristic search method to find optimal network parameters for ELM. The optimized network parameters will enhance the classification accuracy and generalization performance of ELM. Experiments and comparisons on 22 benchmark data sets demonstrate that M-ELM is able to provide highly competitive results compared with other state-of-the-art varieties of ELM algorithms.