An Open-Ended Computational Evolution Strategy for Evolving Parsimonious Solutions to Human Genetics Problems

Abstract

In human genetics a primary goal is the discovery of genetic factors that predict individual susceptibility to common human diseases, but this has proven difficult to achieve because these diseases are likely to result from the joint failure of two or more interacting components. Currently geneticists measure genetic variations from across the genomes of individuals with and without the disease. The association of single variants with disease is then assessed. Our goal is to develop methods capable of identifying combinations of genetic variations predictive of discrete measures of health in human population data. “Artificial evolution” approaches loosely based on real biological processes have been developed and applied, but it has recently been suggested that “computational evolution” approaches will be more likely to solve problems of interest to biomedical researchers. Here we introduce a method to evolve parsimonious solutions in an open-ended computational evolution framework that more closely mimics the complexity of biological systems. 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 drive evolution towards general solutions. We show that this method leads to smaller solutions and does not reduce the power of an open-ended computational evolution system. This method of environmental perturbation fits within the computational evolution framework and is an effective method of evolving parsimonious solutions.