Bioprocess Modeling Using Genetic Programming Based on a Double Penalty Strategy

Abstract

Using genetic programming (GP) integrated with nonlinear parameter estimation we can identify the model for avermectin process. In order to reduce the effect caused by bloating which appears when a GP run stagnates in the later period, a fitness function with a double penalty strategy is proposed. GP with this penalty strategy is less sensitive to the choice of penalty parameters and compromises the fitness and the complexity of an individual, so the method can save considerable amounts of computational effort and find models with better quality. In addition, we combine the mechanism knowledge of the fermentation in GP to increase the quality of population and the convergence speed. Experiments prove that this method outperforms standard GP in reducing computational effort and finding better models more quickly.