Name
Abstract | ||
|---|---|---|
Over the past two decades, machine learning has been gaining significant attention for solving complex engineering problems. Genetic programing (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 pre-defined 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. This paper provides 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10462-020-09894-7 | ARTIFICIAL INTELLIGENCE REVIEW |
Keywords | DocType | Volume |
Civil engineering, Prediction, Classification, Genetic programming, Machine learning, Deep learning | Journal | 54 |
Issue | ISSN | Citations |
3 | 0269-2821 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianyun Zhang | 1 | 2 | 4.10 |
| Kaveh Barri | 2 | 0 | 0.34 |
| Pengcheng Jiao | 3 | 1 | 2.72 |
| Hadi Salehi | 4 | 1 | 1.37 |
| Amir Hossein Alavi | 5 | 1016 | 45.59 |