Instruction-Matrix-Based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Li:2008:ieeeTSMCB,
  author =       "Gang Li and Jin Feng Wang and Kin Hong Lee and 
                 Kwong-Sak Leung",
  title =        "Instruction-Matrix-Based Genetic Programming",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part B: Cybernetics",
  year =         "2008",
  month =        aug,
  volume =       "38",
  number =       "4",
  pages =        "1036--1049",
  keywords =     "genetic algorithms, genetic programming, benchmark
                 classification problems, condition matrix,
                 instruction-matrix-based genetic programming,
                 multiclass classification problems, program trees, rule
                 learning, tree nodes, feature extraction, learning
                 (artificial intelligence), matrix algebra, pattern
                 classification, trees (mathematics), Algorithms,
                 Artificial Intelligence, Computer Simulation, Feedback,
                 Models, Genetic, Models, Theoretical, Pattern
                 Recognition, Automated, Programming, Linear, Systems
                 Theory",
  DOI =          "doi:10.1109/TSMCB.2008.922054",
  ISSN =         "1083-4419",
  abstract =     "In genetic programming (GP), evolving tree nodes
                 separately would reduce the huge solution space.
                 However, tree nodes are highly interdependent with
                 respect to their fitness. In this paper, we propose a
                 new GP framework, namely, instruction-matrix (IM)-based
                 GP (IMGP), to handle their interactions. IMGP maintains
                 an IM to evolve tree nodes and subtrees separately.
                 IMGP extracts program trees from an IM and updates the
                 IM with the information of the extracted program trees.
                 As the IM actually keeps most of the information of the
                 schemata of GP and evolves the schemata directly, IMGP
                 is effective and efficient. Our experimental results on
                 benchmark problems have verified that IMGP is not only
                 better than those of canonical GP in terms of the
                 qualities of the solutions and the number of program
                 evaluations, but they are also better than some of the
                 related GP algorithms. IMGP can also be used to evolve
                 programs for classification problems. The classifiers
                 obtained have higher classification accuracies than
                 four other GP classification algorithms on four
                 benchmark classification problems. The testing errors
                 are also comparable to or better than those obtained
                 with well-known classifiers. Furthermore, an extended
                 version, called condition matrix for rule learning, has
                 been used successfully to handle multiclass
                 classification problems.",
  notes =        "Also known as \cite{4510842}",
}

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

Citations