Modified genetic crossover and mutation operators for sparse regressor selection in NARMAX brain connectivity modeling

In support of a method for nonlinear modeling of cortical connectivity, an innovation in evolutionary processing is reported. By a strategic modification to the crossover and mutation operators within the NSGA-II genetic algorithm, the number of generations required to achieve optimal nonlinear regressor models with 99.9% confidence is reduced by 52%.