A Linear Structured Approach and A Refined Fitness Function in Genetic Programming for Multi-class Object Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{zhang:2007:CS,
  author =       "Mengjie Zhang and Christopher Graeme Fogelberg and 
                 Yuejin Ma",
  title =        "A Linear Structured Approach and A Refined Fitness
                 Function in Genetic Programming for Multi-class Object
                 Classification",
  journal =      "Connection Science",
  year =         "2007",
  volume =       "19",
  number =       "4",
  pages =        "339--359",
  note =         "Special Issue: Evolutionary Learning and
                 Optimisation",
  keywords =     "genetic algorithms, genetic programming, Linear
                 genetic programming, Program structure, Program
                 representation, Fitness function, Multi-class
                 classification, Object classification, Object
                 recognition",
  ISSN =         "0954-0091",
  DOI =          "doi:10.1080/09540090701725557",
  size =         "21 pages",
  abstract =     "This paper describes an approach to the use of genetic
                 programming (GP) to multi-class object recognition
                 problems. Rather than using the standard tree
                 structures to represent evolved classifier programs
                 which only produce a single output value that must be
                 further translated into a set of class labels, this
                 approach uses a linear structure to represent evolved
                 programs, which use multiple target registers each for
                 a single class. The simple error rate fitness function
                 is refined and a new fitness function is introduced to
                 approximate the true feature space of an object
                 recognition problem. This approach is examined and
                 compared with the tree based GP on three data sets
                 providing object recognition problems of increasing
                 difficulty. The results show that this approach
                 outperforms the standard tree based GP approach on all
                 the tasks investigated here and that the programs
                 evolved by this approach are easier to interpret. The
                 investigation into the extra target registers and
                 program length results in heuristic guidelines for
                 initially setting system parameters.",
}

Genetic Programming entries for Mengjie Zhang Christopher Fogelberg Yuejin Ma

Citations