Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming for Multiclass Object Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@TechReport{vuw-CS-TR-05-5,

• author = "Mengjie Zhang and Will Smart",
• title = "Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming for Multiclass Object Classification",
• institution = "Computer Science, Victoria University of Wellington",
• year = "2005",
• number = "CS-TR-05-5",
• address = "New Zealand",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-05-5.abs.html",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-05/CS-TR-05-5.pdf",
• 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.",

}

Genetic Programming entries for Mengjie Zhang Will Smart

Citations