Protein structure optimization with a "Lamarckian" ant colony algorithm

Mark T Oakley; E Grace Richardson; Harriet Carr; Roy L Johnston

doi:10.1109/TCBB.2013.125

Protein structure optimization with a "Lamarckian" ant colony algorithm

Mark T Oakley, E Grace Richardson, Harriet Carr, Roy L Johnston

Chemistry

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

391 Downloads (Pure)

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	https://doi.org/10.1109/TCBB.2013.125
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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10.1109/TCBB.2013.125

Protein Structure Optimisation With a “Lamarckian” Ant Colony Algorithm
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Accepted author manuscript, 2.38 MBLicence: Other (please specify with Rights Statement)

Simulation of Self-Assembly (Via Cambridge)
Johnston, R.
Engineering & Physical Science Research Council
1/10/10 → 30/09/15
Project: Research Councils

Cite this

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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.",

keywords = "Ant colony optimization, coarse-grained model proteins, Bioinformatics, Computational biology, Genetic algorithms, Minimization, Optimization, Proteins",

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AU - Richardson, E Grace

AU - Carr, Harriet

AU - Johnston, Roy L

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AB - 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.

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KW - coarse-grained model proteins

KW - Bioinformatics

KW - Computational biology

KW - Genetic algorithms

KW - Minimization

KW - Optimization

KW - Proteins

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M3 - Article

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JF - IEEE - ACM Transactions on Computational Biology and Bioinformatics

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ER -

Protein structure optimization with a "Lamarckian" ant colony algorithm

Abstract

Keywords

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Simulation of Self-Assembly (Via Cambridge)

Cite this