A Growth-based Optimization Algorithm for Lattice Heteropolymers
Hsiao-Ping Hsu, Vishal Mehra, Walter Nadler, and Peter Grassberger
TL;DR
This paper introduces an enhanced growth-based optimization algorithm for lattice heteropolymers, outperforming existing stochastic methods in finding lowest energy states and discovering previously missed configurations.
Contribution
It presents an improved PERM algorithm that significantly outperforms prior stochastic algorithms in modeling lattice heteropolymers and finding their lowest energy states.
Findings
Outperforms previous PERM versions and other stochastic algorithms.
Finds new lowest energy states missed by earlier methods.
Discusses limitations of the proposed approach.
Abstract
An improved version of the pruned-enriched-Rosenbluth method (PERM) is proposed and tested on finding lowest energy states in simple models of lattice heteropolymers. It is found to outperform not only the previous version of PERM, but also all other fully blind general purpose stochastic algorithms which have been employed on this problem. In many cases it found new lowest energy states missed in previous papers. Limitations are discussed.
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