Techreport,
Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming for Multiclass Object Classification
CS-TR-05-5. Computer Science, Victoria University of Wellington, New Zealand, (2005)
Abstract
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. Rather than
using the best evolved program in a population, this
approach uses multiple programs and a voting strategy
to perform classification. 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. Although developed for object classification,
this approach is expected to be able to be applied to
other classification problems.
