The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.

The tree-based pipeline optimization tool (TPOT) is one of the earliest automated machine learning (ML) frameworks developed for optimizing ML pipelines, with an emphasis on addressing the complexities of biomedical research. TPOT uses genetic programming to explore a diverse space of pipeline structures and hyperparameter configurations in search of optimal pipelines. Here, we provide a comparative overview of the conceptual similarities and implementation differences between the previous and latest versions of TPOT, focusing on two key aspects: (1) the representation of ML pipelines and (2) the underlying algorithm driving pipeline optimization. We also highlight TPOT's application across various medical and healthcare domains, including disease diagnosis, adverse outcome forecasting, and genetic analysis. Additionally, we propose future directions for enhancing TPOT by integrating contemporary ML techniques and recent advancements in evolutionary computation.