- author = "Qinglan Fan",
- title = "Improving Genetic Programming for Image Classification",
- school = "Computer Science, Victoria University of Wellington",
- year = "2023",
- address = "New Zealand",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://openaccess.wgtn.ac.nz/ndownloader/files/49595061",
-
URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Improving_Genetic_Programming_for_Image_Classification/27165381",
-
DOI = "
doi:10.26686/wgtn.27165381",
- size = "328 pages",
-
abstract = "Image classification is a fundamental task in computer
vision. Due to the high dimensionality of the image
data and high variations across images such as
rotation, scale, illumination, and background
variations, developing a good image classification
method is challenging. Moreover, it is typically
difficult and expensive to collect a large number of
labeled images/instances in some computer vision
applications, increasing the difficulty of training
models to achieve high generalization performance. In
addition to improving the classification performance,
reducing the computational cost of an image
classification method is crucial as many existing
methods tend to take a long time to train a
model.
Genetic programming (GP), an evolutionary computation technique, has a flexible representation of variable length and powerful global search ability and can produce potentially interpretable models. GP has been used for image classification. However, the potential of GP in image classification has not been comprehensively investigated in terms of GP representations, i.e., program structures, functions, and terminals. Furthermore, most existing GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications.
The overall goal of this thesis is to address the above issues and further explore the potential of GP in image classification. This goal is achieved by developing GP-based approaches with new representations, new fitness functions, and new genetic operators to improve classification performance and developing new surrogate models to reduce the computational costs of GP-based methods.
...",
-
notes = "Also known as \cite{Fan2024}
Supervisors: Mengjie Zhang and Bing Xue and Ying Bi",
