Using Competitive Co-evolution to Evolve Better Pattern Recognisers

We present a system for the automatic synthesis of classifiers. The CellNet system for generating binary pattern classifiers is used as a base for further experimentation. As in the original CellNet software, we evolve pattern recognisers (hunters). However, in this version called CellNet Co-Ev, we also evolve the patterns (prey) in a competitive co-evolution. Patterns evolve through the application of camouflage functions, which are used to obscure the data naturally found in the database. The addition of this competitive co-evolution yields a larger and more varied database, artificially increasing the difficulty of the classification task. Application to the CEDAR database of handwritten characters shows an increase in the reliability of the evolution of recognisers, as well as in the elimination of over-fitting, relative to the original CellNet software.