21 Ring Optimization of Side Effect Machines

The explosion of available sequence data necessitates the development of sophisticated machine learning tools with which to analyze them. This study investigates the effects of applying a model which simulates the evolution of a ring species to the training of side effect machines. A comparison is done between side effect machines evolved in the ring structure and side effect machines evolved using a standard evolutionary algorithm based on tournament selection. The side effect machines in this study also shift from a training model based on k-means clustering to one based on a k-nearest neighbor model. A parameter study was performed to investigate the impact of the division of training data into examples for nearest neighbor assessment and training cases. The parameter study demonstrated that parameter setting is important in the baseline runs but had little impact in the ring-optimization runs. The ring optimization technique was also found to exhibit substantially better and also more reliable training performance.