Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Finding the Best Regression Subset by Reduction in Nonfull-Rank Cases
SIAM Journal on Matrix Analysis and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Hi-index | 0.00 |
Genetic algorithms (GAs) are increasingly used in large and complex optimisation problems. Here we use GAs to optimise fitness functions related to ridge regression, which is a classical statistical procedure for dealing with a large number of features in a multivariable, linear regression setting. The algorithm avoids overfitting, gracefully handles collinearity and leads to easily interpretable results. We use the method to model the relationship between a quantitative trait and genetic markers in a mouse cross involving 69 F2 mice. The approach will be useful in the context of many genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.