2006 IEEE International Conference on Evolutionary Computation, 2006
Abstract Gene regulatory networks influence development and evolution in living organism. The a... more Abstract Gene regulatory networks influence development and evolution in living organism. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the ...
IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM
This paper presents the impact of twins and the measures for their removal from the population of... more 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.
2006 IEEE International Conference on Evolutionary Computation, 2006
Abstract Gene regulatory networks influence development and evolution in living organism. The a... more Abstract Gene regulatory networks influence development and evolution in living organism. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the ...
IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM
This paper presents the impact of twins and the measures for their removal from the population of... more 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.
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Papers by M. Chetty