Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions

  • Timmy Manning
  • Paul Walsh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7833)

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.

Keywords

CGP CGPANN Wisconsin breast cancer neuroevolution radial basis functions crossover 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Timmy Manning
    • 1
  • Paul Walsh
    • 2
  1. 1.Cork Institute of TechnologyBishopstownIreland
  2. 2.NSilico Ltd.BishopstownIreland

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