  A common claim of evolutionary computation methods is that they can achieve
good results without the need for human intervention. However, one criticism of
this is that there are still hyperparameters which must be tuned in order to
achieve good performance. In this work, we propose a near "parameter-free"
genetic programming approach, which adapts the hyperparameter values throughout
evolution without ever needing to be specified manually. We apply this to the
area of automated machine learning (by extending TPOT), to produce pipelines
which can effectively be claimed to be free from human input, and show that the
results are competitive with existing state-of-the-art which use hand-selected
hyperparameter values. Pipelines begin with a randomly chosen estimator and
evolve to competitive pipelines automatically. This work moves towards a truly
automatic approach to AutoML.

Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning