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)

424 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

Access to Document

10.1109/TCBB.2013.125

Protein Structure Optimisation With a “Lamarckian” Ant Colony Algorithm
(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Eligibility for repository : checked 22/07/2014
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

@article{d9d97290fec24e89a3ac9c1ee7c01e72,

title = "Protein structure optimization with a {"}Lamarckian{"} ant colony algorithm",

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",

author = "Oakley, {Mark T} and Richardson, {E Grace} and Harriet Carr and Johnston, {Roy L}",

year = "2013",

month = nov,

doi = "10.1109/TCBB.2013.125",

language = "English",

volume = "10",

pages = "1548--52",

journal = "IEEE - ACM Transactions on Computational Biology and Bioinformatics",

issn = "1557-9964",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

number = "6",

}

TY - JOUR

T1 - Protein structure optimization with a "Lamarckian" ant colony algorithm

AU - Oakley, Mark T

AU - Richardson, E Grace

AU - Carr, Harriet

AU - Johnston, Roy L

PY - 2013/11

Y1 - 2013/11

N2 - 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.

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.

KW - Ant colony optimization

KW - coarse-grained model proteins

KW - Bioinformatics

KW - Computational biology

KW - Genetic algorithms

KW - Minimization

KW - Optimization

KW - Proteins

U2 - 10.1109/TCBB.2013.125

DO - 10.1109/TCBB.2013.125

M3 - Article

C2 - 24407312

SN - 1557-9964

VL - 10

SP - 1548

EP - 1552

JO - IEEE - ACM Transactions on Computational Biology and Bioinformatics

JF - IEEE - ACM Transactions on Computational Biology and Bioinformatics

IS - 6

ER -

Protein structure optimization with a "Lamarckian" ant colony algorithm

Abstract

Keywords

Access to Document

Fingerprint

Projects

Simulation of Self-Assembly (Via Cambridge)

Cite this