Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{zhang:2006:PRL,

• author = "Mengjie Zhang and Will Smart",
• title = "Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification",
• journal = "Pattern Recognition Letters",
• year = "2006",
• volume = "27",
• number = "11",
• pages = "1266--1274",
• month = aug,
• note = "Evolutionary Computer Vision and Image Understanding",
• keywords = "genetic algorithms, genetic programming, Probability based genetic programming, Object recognition, Object detection, Fitness function, Multiclass classification",
• DOI = "doi:10.1016/j.patrec.2005.07.024",
• abstract = "the use of Gaussian distribution in genetic programming (GP) for 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. 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 multi class 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.",
• notes = "Special Issue on Evolutionary Computer Vision and Image Understanding, Pattern Recognition Letters, An official publication of the International Association for Pattern Recognition",

}

Genetic Programming entries for Mengjie Zhang Will Smart

Citations