A preference-based non-dominated sorting genetic programming for bioprocess modeling

Non-dominated sorting genetic programming is used to make the evaluating of several objectives impersonally. These objectives are the complexity, the oscillation and the training errors of a model. The preference of a decision-maker is integrated into non-dominated sorting GP and then a preference-based non-dominated sorting genetic programming is proposed. In order to improving the searching efficiency, decision-maker´s preference is used to guide the searching direction. Last, several models are selected from the pareto front based on their performance on each objective and an integrated model is obtained. The approach is used to model the biomass concentration and its effectiveness are demonstrated.