Identification of response surface models using genetic programming

By TL Lew, A. Spencer, Fabrizio Scarpa, Keith Worden, A. Rutherford, François M. Hemez. 2006

49 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/88859b276bbb08d2d5f4a3ccdd17aea036c0d771

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. Keith Worden
2. Patrick Siarry
3. Jeremy E. Oakley
4. Anthony OʼHagan
5. Yaochu Jin
6. Handing Wang
7. Yi-Tung Kao
8. Erwie Zahara

In Molecular Biology

1. Patrick Siarry

Citations in the corpus

• Metaheuristics (2016, background): “Besides the work of Koza, several studies [11, 28, 34, 38] have shown the benefit of the application of genetic programming in solving symbolic regression problems.”
• Geometric Generalisation of Surrogate Model Based Optimisation to Combinatorial Spaces (2011, methodology): “[8], in which Genetic Programming is used to do symbolic regression to determine the best fitting vector-input function to the data-points.”
• Probabilistic uncertainty analysis of an FRF of a structure using a Gaussian process emulator (2011, methodology): “Techniques used for building nonstatistical metamodels include neural networks (El Tabach et al., 2007), support vector machines (Drucker et al., 1997; Wang et al., 2005), and genetic programming (Lew et al., 2006).”