In order to comprehend the advantages and short-comings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational complexity. To the best of our knowledge a study like this has not been put forward before and it is essential for the understanding of the nature of the model-building problem of MOEDAs and how they should be improved to achieve a quantum leap in their problem solving capacity.