Pulp suspensions are non-Newtonian and possess a significant yield stress, which creates considerable deviations from ideal mixing as demonstrated by dynamic tests made on both industrial and scale-model pulp chests. Due to the existence of non-ideal flows in agitated pulp chests, the determination of their discrete-time characterization parameters is very challenging. This paper optimally determines these parameters using a multi-parameter hybrid genetic algorithm specially developed to generate robust and high quality results. The algorithm integrates the genetic operations of selection, crossover and mutation with gradient search inside successively expanding and contracting parameter domains using alternating logarithmic and linear mappings. The algorithm is successfully tested on three different sets of simulated data, and it is found to retrieve the model parameters with high accuracy. Three sets of experimental data are then processed by the algorithm for the optimal determination of their characterization parameters. The corresponding optimal outputs match well with their experimental counterparts. The results indicate the potential application of the algorithm to solve non-linear process optimization problems with high accuracies.