2016
DOI: 10.1007/s00894-016-3104-z
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Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

Abstract: This paper presents a differential evolution algorithm that is adapted for the protein folding optimization on a three-dimensional AB off-lattice model. The proposed algorithm is based on a self-adaptive differential evolution that improves the algorithm efficiency and reduces the number of control parameters. A mutation strategy for the fast convergence is used inside the algorithm. A temporal locality is used in order to speed up the algorithm convergence additionally and to find amino-acid conformations wit… Show more

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Cited by 17 publications
(22 citation statements)
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“…In this paper, we extend our Differential Evolution algorithm [4] with two new mechanisms. The first mechanism is a local search that improves convergence speed and reduces runtime complexity for solution evaluation within a specific neighborhood.…”
Section: Methodsmentioning
confidence: 99%
“…In this paper, we extend our Differential Evolution algorithm [4] with two new mechanisms. The first mechanism is a local search that improves convergence speed and reduces runtime complexity for solution evaluation within a specific neighborhood.…”
Section: Methodsmentioning
confidence: 99%
“…To test whether this is true protein structure optimization using DE was implemented. DE was shown to be the best known optimization method for this problem [7]. Specifications of implemented DE algorithm was taken from [7], but without parameter control.…”
Section: Methodsmentioning
confidence: 99%
“…DE was shown to be the best known optimization method for this problem [7]. Specifications of implemented DE algorithm was taken from [7], but without parameter control. DE type was best/1/bin, population size was 100, mutation with dithering was employed with mutation constant taken between 0.1 and 1 and recombination constant was 0.9.…”
Section: Methodsmentioning
confidence: 99%
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“…Na literatura, diversas meta-heurísticas foram propostas para otimizar o modelo 3D AB Off-Lattice. Os autores [Bošković and Brest 2016] propuseram o DE PFO , que consiste no algoritmo DE com autoajuste de parâmetros (jDE) e com técnica de reinicialização global para poder contornar a perda de diversidade. Em [Bošković and Brest 2018], o algoritmo DE LSRC aperfeiçoa a rotina de reinicialização e adiciona um método de busca local que desloca aminoácidos e preserva o restante da conformação da proteína.…”
Section: Introductionunclassified