Evolutionary and genetic optimization of NMR gradient and shim coils

Evolutionary and genetic stochastic optimizations were explored for designing NMR gradient and shim coils. Together with their standard implementations, some hierarchical self-adaptible algorithms, based on a concept of meta-optimization, were tested. Time-demands and quality of the solutions were compared for various optimizers and a broad range of contrasting features was found for different settings of their governing parameters. Thus, an optimal setting of governing parameters was identified as the major challenge for future investigations and improvements of the optimizers.