Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
An introduction to genetic algorithms
An introduction to genetic algorithms
Symbolic Discriminant Analysis for Mining Gene Expression Patterns
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Cross validation consistency for the assessment of genetic programming results in microarray studies
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Comparison of neural network optimization approaches for studies of human genetics
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. Identifying gene-gene and gene-environment interactions which comprise the genetic architecture for a majority of common diseases is a difficult challenge. To this end, novel computational approaches have been applied to studies of human disease. Previously, a GP neural network (GPNN) approach was employed. Although the GPNN method has been quite successful, a clear comparison of GPNN and GP alone to detect genetic effects has not been made. In this paper, we demonstrate that using NN evolved by GP can be more powerful than GP alone. This is most likely due to the confined search space of the GPNN approach, in comparison to a free form GP. This study demonstrates the utility of using GP to evolve NN in studies of the genetics of common, complex human disease.