Genetic Algorithms and Their Use in Chemistry

By Richard S. Judson. 2007

51 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/2f3d2dc96d1403bf954668f3a33e99907523ae18

How researchers see this paper

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

In Computational Theory and Mathematics

1. Douglas B. Kell
2. Ariana Torres-Knoop
3. David Dubbeldam
4. David Broadhurst
5. Krista S. Walton
6. Robert Powers
7. Bradley Worley

In Molecular Biology

1. David Broadhurst
2. Douglas B. Kell
3. Ariana Torres-Knoop
4. David Dubbeldam
5. Krista S. Walton
6. David S. Wishart
7. Royston Goodacre
8. Robert Powers

In Spectroscopy

1. Douglas B. Kell
2. Ariana Torres-Knoop
3. David Dubbeldam
4. David Broadhurst
5. Krista S. Walton
6. David S. Wishart
7. Robert Powers
8. Bradley Worley

Citations in the corpus

• On the inner workings of Monte Carlo codes (2013, methodology): “A common approach is to use genetic algorithms (GA).[15] Tafipolsky et al.”
• Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, methodology): “…algorithms to 'evolve' SBSs or their equivalent have proved very successful for chemical problems (Lucasius and Kateman, 1994; Lucasius et al., 1994; Horchner and Kalivas, 1995; Broadhurst et al., 1997; Judson, 1997; Shaffer and Small, 1997; Gillet et al., 2002; Jarvis and Goodacre, 2005).”