Abstract-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.
They also introduced a twin-removal operator REF to remove duplicates from the population and to prevent premature convergence.
Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model
IEEE/ACM Transactions on Computational Biology and Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Computer Science Medicine