Performance assessment of genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of seismic ultrasonic attenuation

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@Article{kumar:2013:ES,

• author = "Manoj Kumar and Manav Mittal and Pijush Samui",
• title = "Performance assessment of genetic programming {(GP)} and minimax probability machine regression {(MPMR)} for prediction of seismic ultrasonic attenuation",
• journal = "Earthquake Science",
• year = "2013",
• volume = "26",
• number = "2",
• keywords = "genetic algorithms, genetic programming, Seismic attenuation, Minimax probability machine regression, Artificial neural network, ANN, Prediction",
• URL = "http://link.springer.com/article/10.1007/s11589-013-0018-z",
• DOI = "doi:10.1007/s11589-013-0018-z",
• size = "4 pages",
• abstract = "The determination of seismic attenuation(s) (dB/cm) is a challenging task in earthquake science. This article employs genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of s. GP is developed based on genetic algorithm. MPMR maximises the minimum probability of future predictions being within some bound of the true regression function. Porosity (n) (percent), permeability (k) (millidarcy), grain size (d)(lm), and clay content(c) (percent) have been considered as inputs of GP and MPMR. The output of GP and MPMR is s. The developed GP gives an equation for prediction of s. The results of GP and MPMR have been compared with the artificial neural net-work. This article gives robust models based on GP and MPMR for prediction of s.",
• notes = "National Institute of Rock Mechanics, Kolar Gold Fields, 563117, Karnataka, India",

}

Genetic Programming entries for Manoj Kumar Manav Mittal Pijush Samui

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