Grammar Model-based Program Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{shan:2004:gmpe,
  title =        "Grammar Model-based Program Evolution",
  author =       "Yin Shan and Robert I. McKay and Rohan Baxter and 
                 Hussein Abbass and Daryl Essam and Nguyen Xuan Hoai",
  pages =        "478--485",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
                 Computation",
  year =         "2004",
  publisher =    "IEEE Press",
  month =        "20-23 " # jun,
  address =      "Portland, Oregon",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Theory of
                 evolutionary algorithms",
  URL =          "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/shan/scfgcec04.pdf",
  abstract =     "In Evolutionary Computation, fixed genetic operators
                 may destroy the sub-solution, usually called building
                 blocks, instead of discovering and preserving them. One
                 way to overcome this problem is to build a model based
                 on the good individuals, and sample this model to
                 obtain the next population. In this paper, along this
                 line, we propose a new method, Grammar Model-based
                 Program Evolution (GMPE) to evolved GP program. We
                 replace common GP genetic operator with a Probabilistic
                 Context-free Grammar (SCFG). In each generation, an
                 SCFG is learnt, and a new population is generated by
                 sampling this SCFG model. On two benchmark problems we
                 have studied, GMPE significantly outperforms
                 conventional GP, learning faster and more reliably.",
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",
}

Genetic Programming entries for Yin Shan R I (Bob) McKay Rohan Baxter Hussein A Abbass Daryl Essam Nguyen Xuan Hoai