Using Particle Swarm Optimization and Genetic Programming to Evolve Classification Rules

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Yan:2006:WCICA,

• author = "Liping Yan and Jianchao Zeng",
• title = "Using Particle Swarm Optimization and Genetic Programming to Evolve Classification Rules",
• booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006",
• year = "2006",
• volume = "1",
• pages = "3415--3419",
• address = "Dalian",
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-4244-0332-4",
• DOI = "doi:10.1109/WCICA.2006.1713002",
• abstract = "According to analysing particle swarm optimisation (PSO), the structure of genetic programming (GP) and classifier model, PSO algorithm and GP were made to combine to evolve classification rules. Rules were described as binary tree which non-leaf node denoted rule structure and leaf-node was correspond to rule value. Leaf node and non-leaf node employed different evolutionary strategy. First, PSO was applied to evolve leaf node in order to obtain the optimum rule of certain structure, then GP was adopted to optimise rule structure. The best rules were obtained after the twice optimisation. Finally, the new method indicated efficiency through experiments on several datasets of UCI",
• notes = "China North Univ., Taiyuan",

}

Genetic Programming entries for Liping Yan Jianchao Zeng

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