CellNet Co-Ev: Evolving Better Pattern Recognizers Using Competitive Co-evolution

A model for the co-evolution of patterns and classifiers is presented. The CellNet system for generating binary classifiers is used as a base for experimentation. The CellNet system is extended to include a competitive coevolutionary Genetic Algorithm, where patterns evolve as well as classifiers; This is facilitated by the addition of a set of topologically-invariant camouflage functions, through which images may disguise themselves. This allows for the creation of a larger and more varied image database, and also artificially increases the difficulty of the classification problem. Application to the CEDAR database of hand-written characters yields both an increase in reliability and an elimination of over-fitting relative to the original CellNet project.

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Taras Kowaliw, Nawwaf N. Kharma, Chris Jensen, Hussein Moghnieh, Jie Yao
• Keywords: Binary Classifiers, Coevolutionary Genetic Algorithm, GECCO 2004, Topologically-invariant Camouflage Functions