Improving the performance of CGPANN for breast cancer diagnosis using crossover and radial basis functions

Authors:
Timmy Manning;Paul Walsh
Affiliations:
Cork Institute of Technology, Bishopstown, Cork, Ireland;NSilico Ltd., Bishopstown, Cork, Ireland
Venue:
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2013

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Abstract

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.