GPSO: A Framework for Optimization of Genetic Programming Classifier Expressions for Binary Classification Using Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Jabeen:2012:ijicic,

• author = "Hajira Jabeen and Abdul Rauf Baig",
• title = "GPSO: A Framework for Optimization of Genetic Programming Classifier Expressions for Binary Classification Using Particle Swarm Optimization",
• journal = "International journal of innovative computing, information and control",
• year = "2012",
• volume = "8",
• number = "1 A",
• pages = "233--242",
• month = jan,
• keywords = "genetic algorithms, genetic programming, classification, particle swarm optimisation, optimisation, expressions",
• ISSN = "1349-418X",
• publisher = "ICIC international",
• URL = "http://www.ijicic.org/ijicic-10-06097.pdf",
• size = "10 pages",
• abstract = "Genetic Programming (GP) is an emerging classification tool known for its flexibility, robustness and lucidity. However, GP suffers from a few limitations like long training time, bloat and lack of convergence. In this paper, we have proposed a hybrid technique that overcomes these drawbacks by improving the performance of GP evolved classifiers using Particle Swarm Optimisation (PSO). This hybrid classification technique is a two-step process. In the first phase, we have used GP for evolution of arithmetic classifier expressions (ACE). In the second phase, we add weights to these expressions and optimise them using PSO. We have compared the performance of proposed frame- work (GPSO) with the GP classification technique over twelve benchmark data sets. The results conclude that the proposed optimisation strategy outperforms GP with respect to classification accuracy and less computation.",

}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

Citations