Auto-Programming for Numerical Data Based on Remnant-Standard-Deviation-Guided Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Auto-Programming for Numerical Data Based on
                 Remnant-Standard-Deviation-Guided Gene Expression
  author =       "Tao Zeng and Yintian Liu and Xirong Ma and 
                 Xiaoyuan Bao and Jiangtao Qiu and Lixin Zhan",
  booktitle =    "Fifth International Conference on Natural Computation,
                 ICNC '09",
  year =         "2009",
  editor =       "Haiying Wang and Kay Soon Low and Kexin Wei and 
                 Junqing Sun",
  month =        "14-16 " # aug,
  volume =       "3",
  pages =        "124--128",
  address =      "Tianjian, China",
  publisher =    "IEEE Computer Society",
  isbn13 =       "978-0-7695-3736-8",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, automatic programming,
                 mathematical model, fitness evaluation, reverse polish
  bibdate =      "2010-01-21",
  bibsource =    "DBLP,
  DOI =          "doi:10.1109/ICNC.2009.617",
  abstract =     "Automatically numerical data modeling and computer
                 code generation is significant for data mining, data
                 reverse engineering, engineering applications, etc. On
                 auto-programming for numerical data, a new approach,
                 Remnant-standard-Deviation-guided Gene Expression
                 Programming (RD-GEP), was proposed. New individual
                 structure, the K-expression to Reverse Polish Notation
                 code generation without expression tree construction
                 algorithm (K2RPN), and remnant-standard-deviation based
                 fitness evaluation method in RD-GEP were presented and
                 studied. New individual structure makes easy to I/O or
                 storage the candidate solution. New decoding algorithm
                 with linear-time complexity can simplify system
                 operation and unify I/O format. New evaluation
                 mechanism can reduce hypothesis solution space to
                 improve system performance and precision. Feasibility
                 and usability of RD-GEP were verified on various
                 synthetic data sets and real 'Fishcatch' data set.
                 Experimental results showed RD-GEP is good at
                 automatically modeling numerical data and generating
                 reverse polish notation for target model.",

Genetic Programming entries for Tao Zeng Jiangtao Qiu Xirong Ma Xiaoyuan Bao Jiangtao Qiu Lixin Zhan