The following two papers were accepted for publication and presentation as part of the 2009 European Conference on Artificial Life (ECAL'09
) to be held in Budapest
in September. Hope to see you there!
Greene CS, Hill DP, Moore JH. An Open-Ended Computational Evolution Strategy for Evolving Parsimonious Solutions to Human Genetics Problems. Lecture Notes in Computer Science, in press (2009).
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.
Gilmore JM, Greene CS, Andrews PC, Moore JH. An Analysis of New Expert Knowledge Scaling Methods for Biologically Inspired Computing. Lecture Notes in Computer Science, in press (2009).
High-throughput genotyping has made genome-wide data on human genetic variation commonly available, however, finding associations between specific variations and common diseases has proven difficult. The size of these datasets presents an informatics challenge because exhaustive searching for even only pair-wise interactions is computationally expensive. Instead, search methods must be used which efficiently and effectively mine these datasets. Furthermore, individual susceptibility to common diseases likely depends on gene-gene interactions, i.e. epistasis, and not merely on independent genes. To meet these challenges, we turn to a biologically inspired ant colony optimization strategy. We have previously developed an ant system which allows the incorporation of expert knowledge as heuristic information. One method of scaling expert knowledge to probabilities usable in the algorithm, an exponential distribution function which respects intervals between raw expert knowledge scores, has been previously examined. Here, we develop and evaluate three additional expert knowledge scaling methods and find parameter sets for each which maximize power.