Training Finite State Classifiers to Improve PCR Primer Design. Preservation
Working Manuscript

Daniel Ashlock, Kenneth M. Bryden, Steve A. Corns, Patrick Schnable, and T.J. Wen

Abstract PDF eprint

We present results on training finite state machines as classifiers for polymerase chain reaction primers. The goal is to decrease the number of primers that fail to amplify correctly. Finite state classifiers are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multi-population hybridization The system presented here creates a post-production add-on to a standard primer picking program intended to compensate for organism and lab specific factors.