A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP

Fathy Z. Amer, Ahmed M. El-Garhy, Medhat H. Awadalla, Samia M. Rashad, Asmaa K. Abdien

Research output: Contribution to journalArticle

3 Citations (Scopus)


Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and σ, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.

Original languageEnglish
Pages (from-to)323-332
Number of pages10
Issue number4
Publication statusPublished - 2011



  • Fault classification
  • Genetic algorithm (GA)
  • Machine learning
  • Multi fault classification
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Safety, Risk, Reliability and Quality
  • Waste Management and Disposal
  • Condensed Matter Physics
  • Instrumentation
  • Nuclear and High Energy Physics

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