Environmental noise improves epistasis models of genetic data discovered using a computational evolution system

Common human diseases likely result from nonlinear interactions between multiple DNA sequence variations. One goal of human genetics is to use data mining and machine learning methods to identify combinations of genetic variations that are predictive of discrete measures of health in human population data. "Artificial evolution" approaches loosely based on real biological processes have been developed and applied in this domain, but it has recently been suggested that "computational evolution" approaches which incorporate additional biological and evolutionary complexity into existing algorithms will be more likely to solve problems of interest to biologists and biomedical researchers. Here we introduce a method to evolve compact solutions by adding environmental noise to a dataset during fitness evaluation. In ecological systems a highly specialized organism can fail to thrive as the environment changes. By introducing numerous small changes into training data, i.e. the environment, during evolution we similarly drive selection towards more general solutions. We show that this improves the power of the computational evolution system when modest amounts of noise are used. Furthermore, this method of changing the environment in which fitness is evaluated with small perturbations fits within the computational evolution framework and is an effective method of controlling solution size for problems where the data are likely to be noisy.