A Genetic Programming Approach With Building Block Evolving and Reusing to Image Classification

Genetic programming (GP) has shown promising results in image classification in the last decade. However, most existing GP-based image classification methods often have a complex tree/program structure and a large search space, which may lead to poor performance. To address this, this article develops a two-stage-based GP approach to automatically evolving solutions/ensembles for image classification. In the new approach, the process of constructing an image classification solution is divided into two stages, i.e., evolving small building blocks for feature extraction and evolving ensembles of classifiers by reusing these blocks. Accordingly, at each stage, a simple tree structure can be designed to facilitate the search. In the first stage, a simple block representation and a new search mechanism including a population updating strategy are developed to evolve diverse and effective blocks. In the second stage, a small set of diverse blocks are selected and transformed into primitives, which produces a new tree representation to evolve ensembles of classifiers for image classification. The new designs allow the proposed approach to construct sufficiently but not over complex solutions for difficult tasks by using/searching small trees. The new approach outperforms most GP-based and non-GP-based comparison methods on five image datasets, including CIFAR10, Fashion_MNIST, and SVHN. Deep analysis is conducted to provide more insights into the proposed approach.