abstract = "This paper describes a probability based genetic
programming (GP) approach to multiclass object
classification problems. Instead of using predefined
multiple thresholds to form different regions in the
program output space for different classes, this
approach uses probabilities of different classes,
derived from Gaussian distributions, to construct the
fitness function for classification. Two fitness
measures, overlap area and weighted distribution
distance, have been developed. The approach is examined
on three multiclass object classification problems of
increasing difficulty and compared with a basic GP
approach. The results suggest that the new approach is
more effective and more efficient than the basic GP
approach. While the area measure was a bit more
effective than the distance measure in most cases, the
distance measure was more efficient to learn good
program classifiers.",