Assessment of artificial neural network and genetic programming as predictive tools

By Amir H. Gandomi, David A. Roke. Advances in Engineering Software, 2015

375 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f3e04e10e0bf4265cf4433ff4a57e961db8b2bc0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Rayed Alyousef
2. Hisham Alabduljabbar
3. Muhammad Faisal Javed
4. Muhammad Izhar Shah
5. Furqan Farooq
6. Muhammad Khizar Khan
7. Shazim Ali memon
8. Fahid Aslam

In Statistics and Probability

1. Muhammad Faisal Javed
2. Fahid Aslam

Citations in the corpus

• Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020, background): “Various researchers have suggested that the performance of the proposed model considerably depends on the ratio of data points to the number of inputs [72,73].”
• Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, methodology): “+e distribution of explanatory variables on wide range guarantees the best performance of themodel [109].”