A genetic programming approach to the automated design of CNN models for image classification and video shorts creation

Neural architecture search (NAS) is a rapidly growing field which focuses on the automated design of neural network architectures. Genetic algorithms (GAs) have been predominantly used for evolving neural network architectures. Genetic programming (GP), a variation of GAs that work in the program space rather than a solution space, has not been as well researched for NAS. This paper aims to contribute to the research into GP for NAS. Previous research in this field can be divided into two categories. In the first each program represents neural networks directly or components and parameters of neural networks. In the second category each program is a set of instructions, which when executed, produces a neural network. This study focuses on this second category which has not been well researched. Previous work has used grammatical evolution for generating these programs. This study examines canonical GP for neural network design (GPNND) for this purpose. It also evaluates a variation of GP, iterative structure-based GP (ISBGP) for evolving these programs. The study compares the performance of GAs, GPNND and ISBGP for image classification and video shorts creation. Both GPNND and ISBGP were found to outperform GAs, with ISBGP producing better results than GPNND for both applications. Both GPNND and ISBGP produced better results than previous studies employing grammatical evolution on the CIFAR-10 dataset.Open access funding provided by University of Pretoria. This work was funded as part of the Multichoice Research Chair in Machine Learning at the University of Pretoria, South Africa. This work is based on the research supported in part by the National Research Foundation of South Africa.http://link.springer.com/journal/10710hj2024Computer ScienceSDG-09: Industry, innovation and infrastructur

A Genetic Programming Approach for Computer Vision: Classifying High-level Image Features from Convolutional Layers with an Evolutionary Algorithm

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceComputer Vision is a sub-field of Artificial Intelligence that provides a visual perception component to computers, mimicking human intelligence. One of its tasks is image classification and Convolutional Neural Networks (CNNs) have been the most implemented algorithm in the last few years, with few changes made to the fully-connected layer of those neural networks. Nonetheless, recent research has been showing their accuracy could be improved in certain cases by implementing other algorithms for the classification of high-level image features from convolutional layers. Thus, the main research question for this document is: To what extent does the substitution of the fully-connected layer in Convolutional Neural Networks for an evolutionary algorithm affect the performance of those CNN models? The proposed two-step approach in this study does the classification of high-level image features with a state-of-the-art GP-based algorithm for multiclass classification called M4GP. This is conducted using secondary data with different characteristics, to better benchmark the implementation and to carefully investigate different outcomes. Results indicate the new learning approach yielded similar performance in the dataset with a low number of output classes. However, none of the M4GP models was able to surpass the results of the fully-connected layers in terms of test accuracy. Even so, this might be an interesting route if one has a powerful computer and needs a very light classifier in terms of model size. The results help to understand in which situation it might be beneficial to perform a similar experimental setup, either in the context of a work project or concerning a novel research topic

An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

© 2019 IEEE. Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP