  Missing data has a ubiquitous presence in real-life applications of machine
learning techniques. Imputation methods are algorithms conceived for restoring
missing values in the data, based on other entries in the database. The choice
of the imputation method has an influence on the performance of the machine
learning technique, e.g., it influences the accuracy of the classification
algorithm applied to the data. Therefore, selecting and applying the right
imputation method is important and usually requires a substantial amount of
human intervention. In this paper we propose the use of genetic programming
techniques to search for the right combination of imputation and classification
algorithms. We build our work on the recently introduced Python-based TPOT
library, and incorporate a heterogeneous set of imputation algorithms as part
of the machine learning pipeline search. We show that genetic programming can
automatically find increasingly better pipelines that include the most
effective combinations of imputation methods, feature pre-processing, and
classifiers for a variety of classification problems with missing data.
