Improved Pattern Recognition with Artificial Clonal Selection?

In this paper, we examine the clonal selection algorithm CLONALG and the suggestion that it is suitable for pattern recognition. CLONALG is tested over a series of binary character recognition tasks and its performance compared to a set of basic binary matching algorithms. A number of enhancements are made to the algorithm to improve its performance and the classification tests are repeated. Results show that given enough data CLONALG can successfully classify previously unseen patterns and that adjustments to the existing algorithm can improve performance.