Estimation of parameters of a biochemically based model of photosynthesis using a genetic algorithm

Photosynthesis response to carbon dioxide concentration can provide data on a number of important parameters related to leaf physiology. The genetic algorithm (GA), which is a robust stochastic evolutionary computational algorithm inspired by both natural selection and natural genetics, is proposed to simultaneously estimate the parameters including maximum carboxylation rate allowed by ribulose 1·5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate ( V _cmax), potential light-saturated electron transport rate ( J _max), triose-phosphate utilization (TPU), leaf dark respiration in the light ( R _d) and mesophyll conductance ( g _m)] of the photosynthesis models presented by Farquhar, von Caemmerer and Berry, and Ethier and Livingston. The results show that by properly constraining the parameter bounds the GA-based estimate methods can effectively and efficiently obtain globally (or, at least near globally) optimal solutions, which are as good as or better than those obtained by non-linear curve fitting methods used in previous studies. More complicated problems such as taking the g _m variation response to CO₂ into account can be easily formulated and solved by using GA. The influence of the crossover probability ( P _c), mutation probability ( P _m), population size and generation on the performance of GA was also investigated.