Gene Expression Programming in Problem Solving

By Cândida Ferreira. 2002

639 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4c63e4a79cd1dfaae7cc8b9b5767ec86cadffb32

How researchers see this paper

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

In Artificial Intelligence

1. Rayed Alyousef
2. Hisham Alabduljabbar
3. Chun Kiat Chang
4. Nor Azazi Zakaria
5. Muhammad Faisal Javed
6. Muhammad Izhar Shah
7. Kusum Deep
8. Swagatam Das

In Molecular Biology

1. Chun Kiat Chang
2. Nor Azazi Zakaria
3. Muhammad Faisal Javed
4. Kusum Deep
5. Swagatam Das
6. Kanchan Rajwar
7. C. S. Leow
8. Aminuddin Ab. Ghani

Citations in the corpus

• An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2013) 222 Gene Expression (GE) Ferreira (2002) 223 Genetic Algorithm (GA) Holland (1991) 224 Genetic Programming (GP) Koza et al.”
• Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020, background): “Furthermore, during the reproduction stage, the modification in the chromosomes takes place by genetic operators [41,42].”
• Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River (2011, methodology): “As a consequence of this difference, the complete genotype/phenotype GEP system surpasses the older GP system by a factor of 100– 60,000 ( Ferreira 2001a, b).”