TY - GEN
T1 - Issues in evolving GP based classifiers for a pattern recognition task
AU - Teredesai, Ankur M.
AU - Govindaraju, Venu
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/4344611863
M3 - Conference contribution
AN - SCOPUS:4344611863
SN - 0780385152
SN - 9780780385153
T3 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
SP - 509
EP - 515
BT - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
T2 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Y2 - 19 June 2004 through 23 June 2004
ER -