Using loops in genetic programming for a two class binary image classification problem

Li, X and Ciesielski, V 2004, 'Using loops in genetic programming for a two class binary image classification problem', in G. Webb and X. Yu (ed.) AI 2004: Advances in Artificial Intelligence, Cairns, Australia, 24 November 2004, pp. 898-909.


Document type: Conference Paper
Collection: Conference Papers

Title Using loops in genetic programming for a two class binary image classification problem
Author(s) Li, X
Ciesielski, V
Year 2004
Conference name Australian Joint Conference on Artifical Intelligence
Conference location Cairns, Australia
Conference dates 24 November 2004
Proceedings title AI 2004: Advances in Artificial Intelligence
Editor(s) G. Webb
X. Yu
Publisher Springer
Place of publication Berlin, Germany
Start page 898
End page 909
Total pages 12
Abstract Loops are rarely used in genetic programming (GP), because they lead to massive computation due to the increase in the size of the search space. We have investigated the use of loops with restricted semantics for a problem in which there are natural repetitive elements, that of distinguishing two classes of images. Using our formulation, programs with loops were successfully evolved and performed much better than programs without loops. Our results suggest that loops can successfully used in genetic programming in situations where domain knowledge is available to provide some restrictions on loop semantics.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) genetic programming
image classification
loops
DOI - identifier 10.1007/b104336
Copyright notice © Springer-Verlag Berlin Heidelberg 2004
ISBN 978-3-540-24059-4
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