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Abstract
We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian" genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms.
| Original language | English |
|---|---|
| Pages (from-to) | 1548-52 |
| Number of pages | 5 |
| Journal | IEEE - ACM Transactions on Computational Biology and Bioinformatics |
| Volume | 10 |
| Issue number | 6 |
| Early online date | 8 Oct 2013 |
| DOIs | |
| Publication status | Published - Nov 2013 |
Keywords
- Ant colony optimization
- coarse-grained model proteins
- Bioinformatics
- Computational biology
- Genetic algorithms
- Minimization
- Optimization
- Proteins
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Dive into the research topics of 'Protein structure optimization with a "Lamarckian" ant colony algorithm'. Together they form a unique fingerprint.Projects
- 1 Finished
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Simulation of Self-Assembly (Via Cambridge)
Johnston, R. (Principal Investigator)
Engineering & Physical Science Research Council
1/10/10 → 30/09/15
Project: Research Councils