Function mining based on gene Expression Programming and Particle Swarm Optimization

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@InProceedings{Li:2009:ICCSIT,

• 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 (GEP-RNC).",
• 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

Citations