title: A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes.
abstract: Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset.
  
  
  
  Nine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (χ2).
  
  
  
  The results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest χ2value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest β values (β≥0.32 in XOR disease model and β≥0.04 in ZZ disease model) and the significant p-value (p-value<0.001 in both the XOR and ZZ disease models).
  
  
  
  The Sinai chaotic map was found to effectively enhance the fitness values (χ2) of PSO method, indicating that the Sinai chaotic map combined with PSO method is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models.
authors.affiliation: Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan. Electronic address: chuang@isu.edu.tw.
authors.forename: Li-Yeh
authors.initials: LY
authors.lastname: Chuang
authors.affiliation: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan. Electronic address: moi9009@gmail.com.
authors.forename: Sin-Hua
authors.initials: SH
authors.lastname: Moi
authors.affiliation: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan. Electronic address: e0955767257@yahoo.com.tw.
authors.forename: Yu-Da
authors.initials: YD
authors.lastname: Lin
authors.affiliation: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan. Electronic address: chyang@cc.kuas.edu.tw.
authors.forename: Cheng-Hong
authors.initials: CH
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D016009:Chi-Square Distribution; D006801:Humans; D008957:Models, Genetic; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D020641:Polymorphism, Single Nucleotide
keywords: Chaos; Particle swarm optimization; Single nucleotide polymorphism; Single nucleotide polymorphism barcode detection