Open AccessRandom-walk
Author(s) -
Mahmood A. Rashid,
Swakkhar Shatabda,
M. A. Hakim Newton,
Md Tamjidul Hoque,
Duc Nghia Pham,
Abdul Sattar
Publication year - 2012
Publication title -
griffith research online (griffith university, queensland, australia)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2382936.2383043
Subject(s) - maxima and minima , random walk , tabu search , computer science , population , heuristic , hill climbing , algorithm , local search (optimization) , lattice (music) , face (sociological concept) , mathematical optimization , protein structure prediction , artificial intelligence , mathematics , protein structure , physics , statistics , mathematical analysis , social science , demography , sociology , acoustics , nuclear magnetic resonance
Protein structure prediction is a challenging optimisation problem to the computer scientists. A large number of ex- isting (meta-)heuristic search algorithms attempt to solve the problem by exploring possible structures and findingthe one with minimum free energy. However, these algo-rithms often get stuck in local minima and thus performpoorly on large sized proteins. In this paper, we present a random-walk based stagnation recovery approach. We tested our approach on tabu-based local search as well as population based genetic algorithms. The experimental results show that, random-walk is very effective for escaping from local minima for protein structure prediction on face- centred-cubic lattice and hydrophobic-polar energy model.Griffith Sciences, School of Information and Communication TechnologyFull Tex
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