A hybrid computational approach for seismic energy demand prediction

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@Article{Gharehbaghi:2018:ESA,

• author = "Sadjad Gharehbaghi and A. H. Gandomi and S. Achakpour and Mohammad Nabi Omidvar",
• title = "A hybrid computational approach for seismic energy demand prediction",
• journal = "Expert Systems with Applications",
• year = "2018",
• volume = "110",
• pages = "335--351",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Evolutionary computation, Regression analysis, Input energy, Hysteretic energy, Seismic energy spectra",
• ISSN = "0957-4174",
• URL = "https://eprints.whiterose.ac.uk/156298/1/Manuscript-R2-v4.pdf",
• URL = "http://www.sciencedirect.com/science/article/pii/S0957417418303543",
• DOI = "doi:10.1016/j.eswa.2018.06.009",
• abstract = "In this paper, a hybrid genetic programming (GP) with multiple genes is implemented for developing prediction models of spectral energy demands. A multi-objective strategy is used for maximizing the accuracy and minimizing the complexity of the models. Both structural properties and earthquake characteristics are considered in prediction models of four demand parameters. Here, the earthquake records are classified based on soil type assuming that different soil classes have linear relationships in terms of GP genes. Therefore, linear regression analysis is used to connect genes for different soil types, which results in a total of sixteen prediction models. The accuracy and effectiveness of these models were assessed using different performance metrics and their performance was compared with several other models. The results indicate that not only the proposed models are simple, but also they outperform other spectral energy demand models proposed in the literature.",
• notes = "Also known as \cite{GHAREHBAGHI2018335}",

}

Genetic Programming entries for Sadjad Gharehbaghi A H Gandomi S Achakpour Mohammad Nabi Omidvar

Citations