Twin removal in genetic algorithms for protein structure prediction using low-resolution model.
This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low-resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences, which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.
Griffith University, Nathan campus, 170 Kessels Road, Nathan, Brisbane, Qld 4111, Australia. tamjidul.hoque@gmail.com
Md Tamjidul
MT
Hoque
Madhu
M
Chetty
Andrew
A
Lewis
Abdul
A
Sattar
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D057927:Hydrophobic and Hydrophilic Interactions; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D011506:Proteins