Genetic Algorithm Applied To Simultaneous Parameter Estimation In Bacterial Growth

Several mathematical models have been developed to understand the interactions of microorganisms in foods and predict their growth. The resulting model equations for the growth of interacting cells include several parameters that must be determined for the specific conditions to be modeled. In this study, these parameters were determined by using inverse engineering and a multi-objective optimization procedure that allows fitting more than one experimental growth curve simultaneously. A genetic algorithm was applied to obtain the best parameter values of a model that permit the construction of the front of Pareto with 50 individuals or phenotypes. The method was applied to three experimental data sets of simultaneous growth of lactic acid bacteria (LAB) and Listeria monocytogenes (LM). Then, the proposed method was compared with a conventional mono-objective sequential fit. We concluded that the multi-objective fit by the genetic algorithm gives superior results with more parameter identifiability than the conventional sequential approach.