A New Crossover Operator in GP for Object Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Mengjie Zhang and Xiaoying Gao and Weijun Lou",
  title =        "A New Crossover Operator in GP for Object
  institution =  "Computer Science, Victoria University of Wellington",
  year =         "2006",
  number =       "CS-TR-06-2",
  address =      "New Zealand",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Crossover
                 points, looseness controlled crossover, hybrid search",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-2.abs.html",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-2.pdf",
  abstract =     "instead of randomly choosing the crossover points as
                 in the standard crossover operator, we use a measure
                 called looseness to guide the selection of crossover
                 points. Rather than using the genetic beam search only,
                 this approach uses a hybrid beam-hill climbing search
                 scheme in the evolutionary process. This approach is
                 examined and compared with the standard crossover
                 operator and the headless chicken crossover method on a
                 sequence of object classification problems. The results
                 suggest that this approach outperforms both the
                 headless chicken crossover and the standard crossover
                 on all of these problems.",
  size =         "17 pages",

Genetic Programming entries for Mengjie Zhang Xiaoying (Sharon) Gao Weijun (Norman) Lou