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Published in:
2013 | OriginalPaper | Chapter
Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions
Authors : Timmy Manning, Paul Walsh
Published in: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Publisher: Springer Berlin Heidelberg
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Recently published evaluations of the topology and weight evolving artificial neural network algorithm Cartesian genetic programming evolved artificial neural networks (CGPANN) have suggested it as a potentially powerful tool for bioinformatics problems. In this paper we provide an overview of the CGPANN algorithm and a brief case study of its application to the Wisconsin breast cancer diagnosis problem. Following from this, we introduce and evaluate the use of RBF kernels and crossover to CGPANN as a means of increasing performance and consistency.