Genetic programming in civil engineering: advent, applications and future trends

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

@Article{Zhang:2021:AIR,

• author = "Qianyun Zhang and Kaveh Barri and Pengcheng Jiao and Hadi Salehi and Amir H. Alavi",
• title = "Genetic programming in civil engineering: advent, applications and future trends",
• journal = "Artificial Intelligence Review",
• year = "2021",
• volume = "54",
• pages = "1863--1885",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Civil engineering, Prediction, Classification, Machine learning, Deep learning",
• URL = "https://rdcu.be/cwlIF",
• DOI = "doi:10.1007/s10462-020-09894-7",
• size = "23 pages",
• abstract = "Over the past two decades, machine learning has been gaining significant attention for solving complex engineering problems. Genetic programming (GP) is an advanced framework that can be used for a variety of machine learning tasks. GP searches a program space instead of a data space without a need to predefined models. This method generates transparent solutions that can be easily deployed for practical civil engineering applications. GP is establishing itself as a robust intelligent technique to solve complicated civil engineering problems. We provide a review of the GP technique and its applications in the civil engineering arena over the last decade. We discuss the features of GP and its variants followed by their potential for solving various civil engineering problems. We finally envision the potential research avenues and emerging trends for the application of GP in civil engineering.",
• notes = "Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA",

}

Genetic Programming entries for Qianyun (Gloria) Zhang Kaveh Barri Pengcheng Jiao Hadi Salehi A H Alavi

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