  In this paper, a nonlinear symbolic regression technique using an
evolutionary algorithm known as multi-gene genetic programming (MGGP) is
applied for a data-driven modelling between the dependent and the independent
variables. The technique is applied for modelling the measured global solar
irradiation and validated through numerical simulations. The proposed modelling
technique shows improved results over the fuzzy logic and artificial neural
network (ANN) based approaches as attempted by contemporary researchers. The
method proposed here results in nonlinear analytical expressions, unlike those
with neural networks which is essentially a black box modelling approach. This
additional flexibility is an advantage from the modelling perspective and helps
to discern the important variables which affect the prediction. Due to the
evolutionary nature of the algorithm, it is able to get out of local minima and
converge to a global optimum unlike the back-propagation (BP) algorithm used
for training neural networks. This results in a better percentage fit than the
ones obtained using neural networks by contemporary researchers. Also a
hold-out cross validation is done on the obtained genetic programming (GP)
results which show that the results generalize well to new data and do not
over-fit the training samples. The multi-gene GP results are compared with
those, obtained using its single-gene version and also the same with four
classical regression models in order to show the effectiveness of the adopted
approach.
