.png){kind=icon}
Return
Predicting high-strength concrete parameters using weighted genetic programming
.png){kind=icon}
PRE
AI
DOI：10.1007/s00366-011-0208-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Genetic programming (GP) is an evolutionary algorithm-based methodology that employs a binary tree topology with optimized functional operators. This study introduced weight coefficients to each GP linkage in a tree in order to create a new weighted genetic programming (WGP) approach. Two distinct advantages of the proposed WGP include (1) balancing the influences of the two front input branches and (2) incorporating weights throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies presented herein highlight a high-strength concrete reference study. Results showed that the proposed WGP not only improves GP in terms of introduced weight coefficients, but also provides both accurate results and formula outputs.
Keywords:
Weighted formula
Prediction
High-strength concrete
Genetic programming
Weighted genetic programming
Journal
IF:
4.9
Papers:
2.7K
Citations:
9.3K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more