More multiple worlds evolution for motif discovery

The Multiple Worlds Model of evolution is a spatially structured evolutionary algorithm which uses the ideas of Darwin´s finches as a motivating idea. Through multiple populations separated genetically but with a unified fitness evaluation, Multiple Worlds acts to partition data via specialization. Each of the populations must specialize in order to gain fitness, or a population can be reduced to little or no fitness. Such a drop in fitness implies that the number of populations, each representing a class in the data, is too large. The number of natural classes in the data is discovered by the algorithm via an analog to biological extinction. This study examines the application of this method to discovery of degenerate motifs on two types of data. The first is the classification of synthetic motifs, created by a self-driving finite state machine, selected to yield high-entropy data. The second is a biological example comprising two classes of data drawn from a Human Leukocyte Antigen data set. The classifiers found not only allow for the division of the data, but are expressed as degenerate motifs; granting researchers a comprehensible, reusable result.