Parameter acquirement methods for rule-based model of virtual plant based on optimal algorithms

Rule-based model is an effective technique to dynamically simulate the morphological development of a plant. It is thus used widely in the field of plant modeling and visualization. Before a virtual plant model with high quality performance is established, it is a key step to provide suitable parameters for the rule-based model. There are several disadvantages in the traditional/manual ways to design the model, e.g. with low efficiency. Therefore, how to obtain appropriate parameters for the rule-based model has attracted many researchers devoting themselves to this area. In the past twenty years, Genetic Algorithm and Gene Expression Programming have been used to optimize the production rules of Do L-system and Parametric Do L-system. Due to the complexity of the structure of a plant, researches' attentions are mostly paid in the narrow area of simple plant morphology restrictively. In this study, the parameter-acquired methods for rule-based model, which is based on Genetic Algorithm and Gene Expression Programming, are summarized. And the relative techniques and the possible development in the future are discussed as well.