Name
Abstract | ||
|---|---|---|
This study proposed a novel knowledge discovery method, Genetic Operation Tree (GOT), which is composed of operation tree (OT) and genetic algorithm (GA), to automatically produce self-organized formulas to predict compressive strength of High-Performance Concrete. In GOT, OT plays the architecture to represent an explicit formula, and GA plays the optimization mechanism to optimize the OT to fit experimental data. Experimental data from several different sources were used to evaluate the method. The results showed that GOT can produce formulas which are more accurate than nonlinear regression formulas but less accurate than neural network models. However, neural networks are black box models, while GOT can produce explicit formulas, which is an important advantage in practical applications. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.07.004 | Expert Syst. Appl. |
Keywords | Field | DocType |
concrete material,genetic operation tree,neural network,explicit formula,different source,material,black box model,genetic algorithms,experimental data,neural network model,concrete,knowledge discovery,genetic operation trees,high-performance concrete,compressive strength,operation tree,novel knowledge discovery method,genetic operator,nonlinear regression,self organization,genetic algorithm | Black box (phreaking),Experimental data,Computer science,Nonlinear regression,Knowledge extraction,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
36 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
22 | 1.54 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| I-Cheng Yeh | 1 | 339 | 22.45 |
| Li-Chuan Lien | 2 | 44 | 3.82 |