Understanding evolved genetic programs for a real world object detection problem

Authors:
Victor Ciesielski;Andrew Innes;Sabu John;John Mamutil
Affiliations:
School of Computer Science and Information Technology, RMIT University, Melbourne, Vic, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Vic, Australia;School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University;Braces Pty Ltd, Australia
Venue:
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Year:
2005

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Abstract

We describe an approach to understanding evolved programs for a real world object detection problem, that of finding orthodontic landmarks in cranio-facial X-Rays. The approach involves modifying the fitness function to encourage the evolution of small programs, limiting the function set to a minimal number of operators and limiting the number of terminals (features). When this was done for two landmarks, an easy one and a difficult one, the evolved programs implemented a linear function of the features. Analysis of these linear functions revealed that underlying regularities were being captured and that successful evolutionary runs usually terminated with the best programs implementing one of a small number of underlying algorithms. Analysis of these algorithms revealed that they are a realistic solution to the object detection problem, given the features and operators available.