Shapes Extraction Method by Genetic Algorithm with Local Search Method

2014 ◽  
Author(s):  
Mitsukuni Matayoshi
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Extraction Method ◽  
Search Method ◽  
Local Search Method

scholarly journals Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes

2016 ◽  
Vol 6 (3) ◽  
pp. 203-212 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Yu-Da Lin ◽  
Li-Yeh Chuang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Genetic Association ◽  
Search Method ◽  
P Value ◽  
Statistical Tools ◽  
Chi Square ◽  
Local Search Method ◽  
Association Models ◽  
Chi Square Test

Abstract Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm based on local search method, to detect significant genetic association models amongst large numbers of SNP combinations. We used two disease models to simulate the large data sets considering the minor allele frequency (MAF), number of SNPs, and number of samples. The three-order epistasis models were evaluated by chi-square test (χ2) to evaluate the significance (P-value < 0.05). Analysis results showed that lsGA provided higher chi-square test values than that of GA. Simple linear regression indicated that lsGA provides a significant advantage over GA, providing the highest β values and significant p-value.

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