a hybrid monte carlo ant colony optimization approach for protein structure prediction in the hp model

Clicks: 311
ID: 244951
2013
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #9 of 102 articles by views in ama journal of ethics

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction (PSP) both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo (MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We describe this method and compare results obtained on well known HP instances in the 3 dimensional cubic lattice to those obtained with standard ACO and Simulated Annealing (SA). All methods were implemented using an unconstrained neighborhood and a modified objective function to prevent the creation of overlapping walks. Results show that our methods perform better than the other heuristics in all benchmark instances.
Reference Key
mauri2013electronica Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Giancarlo Mauri;Andrea G. Citrolo
Journal ama journal of ethics
Year 2013
DOI
10.4204/EPTCS.130.9
URL
http://arxiv.org/pdf/1309.7690v1
Keywords
computer science mathematicselectronic computers

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.