Predict Two-Dimensional Protein Folding Based on Hydrophobic-Polar Lattice Model and Chaotic Clonal Genetic Algorithm

被引：3

作者：

Wang, Shuihua ^{[1
]}

Wu, Lenan ^{[2
]}

Huo, Yuankai ^{[1
]}

Wu, Xueyan ^{[1
]}

Wang, Hainan ^{[1
]}

Zhang, Yudong ^{[1
]}

机构：

[1] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China

[2] Southeast Univ, Sch Informat Sci & Engn, Nanjing, Jiangsu, Peoples R China

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2016 | 2016年 / 9937卷

关键词：

Protein folding; Chaotic clonal genetic algorithm; Clonal selection algorithm; Hydrophobic-polar model; Artificial immune system; BIOGEOGRAPHY-BASED OPTIMIZATION; HYBRIDIZATION; NUMBER;

D O I：

10.1007/978-3-319-46257-8_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components-(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.

引用

页码：10 / 17

页数：8

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