Using Instruction Matrix Based Genetic Programming to Evolve Programs

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

  author =       "Gang Li and Kin Hong Lee and Kwong-Sak Leung",
  title =        "Using Instruction Matrix Based Genetic Programming to
                 Evolve Programs",
  booktitle =    "Proceedings of the Second International Symposium on
                 Computation and Intelligence, ISICA 2007",
  year =         "2007",
  editor =       "Lishan Kang and Yong Liu and Sanyou Y. Zeng",
  volume =       "4683",
  series =       "Lecture Notes in Computer Science",
  pages =        "631--640",
  address =      "Wuhan, China",
  month =        sep # " 21-23",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Instruction
                 Matrix based Genetic Programming",
  isbn13 =       "978-3-540-74580-8",
  DOI =          "doi:10.1007/978-3-540-74581-5_69",
  size =         "10 pages",
  abstract =     "In Genetic Programming (GP), evolving tree nodes
                 separately would be an ideal approach to reduce the
                 huge solution space of GP. We use Instruction Matrix
                 based Genetic Programming (IMGP) to evolve tree nodes
                 separately while taking into account their
                 interdependencies in the form of subtrees. IMGP uses an
                 Instruction Matrix (IM) to maintain the statistical
                 data of tree nodes and subtrees. IMGP extracts program
                 trees from IM, and updates IM with the information of
                 the extracted program trees. The experiments have
                 verified that the results of IMGP are better than those
                 the related GP algorithms in terms of the qualities of
                 the solutions and the number of program evaluations.",
  notes =        "'no explicit population' cites \cite{shan:2004:gmpe}
                 \cite{Shan:2003:Pewel} assumes full binary tree
                 (hs-expression) so every part of tree has a known grid
                 position. (Limits three height)

                 'like context preserving crossover'
                 \cite{Dhaeseleer:1994:cpcGP} IMGP matrix shuffle.
                 6-parity also with sin,cos,exp,rlog. Max. Symbolic
                 regression like PIPE? Fitness of primitive by grid
                 position (rather than by neighbours in sub-tree).",
  bibdate =      "2007-08-31",
  bibsource =    "DBLP,

Genetic Programming entries for Gang Li Kin-Hong Lee Kwong-Sak Leung