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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{manning:2013:evobio,

• author = "Timmy Manning and Paul Walsh",
• title = "Improving the Performance of {CGPANN} for Breast Cancer Diagnosis using Crossover and Radial Basis Functions",
• booktitle = "11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2013}",
• year = "2013",
• editor = "Leonardo Vanneschi and William S. Bush and Mario Giacobini",
• month = apr # " 3-5",
• series = "LNCS",
• volume = "7833",
• publisher = "Springer Verlag",
• organisation = "EvoStar",
• address = "Vienna, Austria",
• pages = "165--176",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
• isbn13 = "978-3-642-37188-2",
• DOI = "doi:10.1007/978-3-642-37189-9_15",
• 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.",

}

Genetic Programming entries for Timmy Manning Paul J Walsh

Citations