One concern of AutoML systems is how to discover the best pipeline configuration to solve a particular task in the shortest amount of time. Recent approaches tackle the problem using techniques based on learning a model that helps relate the configuration space and the objective being optimized. However, relying on such a model poses some difficulties. First, both pipelines and datasets have to be represented with meta-features. Second, there exists a strong dependence on the chosen model and it…