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

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

@InProceedings{conf/icnc/ZengLMBQZ09,
  title =        "Auto-Programming for Numerical Data Based on
                 Remnant-Standard-Deviation-Guided Gene Expression
                 Programming",
  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
                 notation",
  bibdate =      "2010-01-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icnc/icnc2009-3.html#RaoWY09",
  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

Citations