An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML
This work addresses the need for reduced human intervention in AutoML, though it is incremental as it builds on existing TPOT methods.
The paper tackles the problem of hyperparameter tuning in evolutionary computation for AutoML by proposing a near parameter-free genetic programming approach that adapts hyperparameters automatically, showing competitive results with state-of-the-art methods that use manual tuning.
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.