Evolution of glass forming ability indicator by genetic programming

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@Article{Tripathi:2016:CMS,

• author = "Manwendra K. Tripathi and Subhas Ganguly and Partha Dey and P. P. Chattopadhyay",
• title = "Evolution of glass forming ability indicator by genetic programming",
• journal = "Computational Materials Science",
• volume = "118",
• pages = "56--65",
• year = "2016",
• ISSN = "0927-0256",
• DOI = "doi:10.1016/j.commatsci.2016.02.037",
• URL = "http://www.sciencedirect.com/science/article/pii/S0927025616300763",
• abstract = "A symbolic regression technique has been employed to evolve the functional relationship among the characteristic transformation temperatures, viz. glass transition temperature (Tg), onset crystallization temperature (Tx) and offset temperature of melting (Tl) concerning glass forming ability (GFA) of bulk metallic glasses (BMGs). The critical diameters (Dmax) of 410 reported BMGs, along with their Tg, Tx and Tl values, forms the training data, for a genetic programming based computer code which attempts to evolve an expression leading to high correlation with Dmax as the target variable. Another set of recently reported 184 BMGs data, is used to assess the performance of the evolved expression. The evolved expression shows significantly improved correlations with critical diameter Dmax, for training data, test data and training and test data considered together. The same also compares well with the high correlation GFA indicators reported earlier in the literature.",
• keywords = "genetic algorithms, genetic programming, Glass forming ability, GFA criteria, Bulk metallic glass",
• notes = "National Institute of Technology Raipur, Raipur 492010, India",

}

Genetic Programming entries for Manwendra K Tripathi Subhas Ganguly Partha Pratim Dey P P Chattopadhyay

Citations