Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification
This work addresses the need for more interpretable and less human-intervention-dependent methods in image classification, though it is incremental as it builds on existing genetic programming techniques.
The paper tackled binary image classification by proposing a hybrid memetic approach combining genetic programming and gradient-based optimization, achieving performance improvements over a baseline without local search on four datasets.
Image classification is an essential task in computer vision, which aims to categorise a set of images into different groups based on some visual criteria. Existing methods, such as convolutional neural networks, have been successfully utilised to perform image classification. However, such methods often require human intervention to design a model. Furthermore, such models are difficult to interpret and it is challenging to analyse the patterns of different classes. This paper presents a hybrid (memetic) approach combining genetic programming (GP) and Gradient-based optimisation for image classification to overcome the limitations mentioned. The performance of the proposed method is compared to a baseline version (without local search) on four binary classification image datasets to provide an insight into the usefulness of local search mechanisms for enhancing the performance of GP.