School of Computing

Typed cartesian genetic programming for image classification

Phil T. Cattani and Colin G. Johnson

In Proceedings of the 2009 UK Workshop on Computational Intelligence, pages 182-196, University of Nottingham, September 2009.


This paper introduces an extension to Cartesian Genetic Programming (CGP), aimed at image classification problems. Individuals in the population consist of two layers of functions: image processing functions, and traditional mathematical functions. Information can be passed between these layers, and the final result can either be an image or a numerical value. This has been applied to image classification, by using CGP to evolve image processing algorithms for feature extraction. This paper presents results which show that these automatically extracted features can substantially increase classification accuracy on a medical problem concerned with the analysis of potentially cancerous cells.

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Bibtex Record

author = {Phil T. Cattani and Colin G. Johnson},
title = {Typed Cartesian Genetic Programming for Image Classification},
month = {September},
year = {2009},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {},
    publication_type = {inproceedings},
    submission_id = {21535_1262704735},
    booktitle = {Proceedings of the 2009 UK Workshop on Computational Intelligence},
    address = {University of Nottingham},
    refereed = {yes},

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