A parallel and non-parallel genetic algorithm for deconvolution of NMR spectra peaks

Interpreting high resolution nuclear magnetic resonance (NMR) spectra of complex samples is investigated by the use of real valued parallel and nonparallel genetic algorithm based on stochastic search procedure. The population-centric crossover operators were used in real coded genetic algorithm (RCGA) based on some probability distributions. This paper also presents parallel genetic algorithms computations with different genetic immigration operators. Different results were found with respect to the problems, even at the different stages of the genetic process in the same problem. It is observed that the grid and centralized type genetic immigration (island models) were effective on the global optimization. The parallel and non-parallel algorithms were also applied for solving some multi-modal test problems. It is found that the island models achieve superior performance on multi-modal test problems and on deconvolution of complex NMR spectra.

• It is a robust algorithm and time saving.
• It is a hybrid global optimization approach.
• Improved with Parallel Genetic Algorithm.
• Applied to deconvolution of peaks in NMR spectra.
• Tested with widely used multimodal test functions.