  Using evolutionary computation algorithms to solve multiple tasks with
knowledge sharing is a promising approach. Image feature learning can be
considered as a multitask problem because different tasks may have a similar
feature space. Genetic programming (GP) has been successfully applied to image
feature learning for classification. However, most of the existing GP methods
solve one task, independently, using sufficient training data. No multitask GP
method has been developed for image feature learning. Therefore, this paper
develops a multitask GP approach to image feature learning for classification
with limited training data. Owing to the flexible representation of GP, a new
knowledge sharing mechanism based on a new individual representation is
developed to allow GP to automatically learn what to share across two tasks and
to improve its learning performance. The shared knowledge is encoded as a
common tree, which can represent the common/general features of two tasks. With
the new individual representation, each task is solved using the features
extracted from a common tree and a task-specific tree representing
task-specific features. To learn the best common and task-specific trees, a new
evolutionary process and new fitness functions are developed. The performance
of the proposed approach is examined on six multitask problems of 12 image
classification datasets with limited training data and compared with three GP
and 14 non-GP-based competitive methods. Experimental results show that the new
approach outperforms these compared methods in almost all the comparisons.
Further analysis reveals that the new approach learns simple yet effective
common trees with high effectiveness and transferability.
