Issues in evolving GP based classifiers for a pattern recognition task

This paper discusses issues when evolving genetic programming (GP) classifiers for a pattern recognition task such as handwritten digit recognition. Developing elegant solutions for handwritten digit classification is a challenging task. Similarly, design and training of classifiers using genetic programming is a relatively new approach in pattern recognition as compared to other traditional techniques. Several strategies for GP training are outlined and the empirical observations are reported. The issues we faced such as training time, a variety of fitness landscapes and accuracy of results are discussed. Care has been taken to test GP using a variety of parameters and on several handwritten digits datasets.