The high level of noise is one of the main challenges in data acquisition of epileptic seizures. Pre-processing and feature extraction are generally adopted, but there is no consensus on which features should be extracted. This work relies on three alternatives for feature extraction and proposes a multi-objective ensemble-based method that automatically finds and aggregates models with distinct influences of each feature extraction procedure, composing a single prediction.