Function mining based on gene Expression Programming and Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Taiyong Li and Tiangang Dong and Jiang Wu and 
                 Ting He",
  title =        "Function mining based on gene Expression Programming
                 and Particle Swarm Optimization",
  booktitle =    "2nd IEEE International Conference on Computer Science
                 and Information Technology, ICCSIT 2009",
  year =         "2009",
  month =        aug,
  pages =        "99--103",
  abstract =     "Gene expression programming (GEP) is a powerful tool
                 widely used in function mining. However, it is
                 difficult for GEP to generate appropriate numeric
                 constants for function mining. In this paper, a novel
                 approach of creating numeric constants, GEPPSO, was
                 proposed, which embedded particle swarm optimization
                 (PSO) into GEP. In the approach, the evolutionary
                 process was divided into 2 phases: in the first phase,
                 GEP focused on optimising the structure of function
                 expression, and in the second one, PSO focused on
                 optimising the constant parameters. The experimental
                 results on function mining problems show that the
                 performance of GEPPSO is better than that of the
                 existing GEP random numerical constants algorithm
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP, PSO, evolutionary process,
                 function mining, particle swarm optimisation, random
                 numerical constants algorithm, data mining, particle
                 swarm optimisation",
  DOI =          "doi:10.1109/ICCSIT.2009.5234621",
  notes =        "Also known as \cite{5234621}",

Genetic Programming entries for Taiyong Li Tiangang Dong Jiang Wu Ting He