Spectral characterization involves building a model that relates the device dependent representation to the reflectance function of the printed color, usually represented with a high number of reflectance samples at different wavelengths. Look-up table-based approaches, conventionally employed for colorimetric device characterization cannot be easily scaled to multispectral representations, but methods for the analytical description of devices are required. The article describes an innovative analytical printer model based on the Yule–Nielsen Spectral Neugebauer equation and formulated with a large number of degrees of freedom in order to account for dot-gain, ink interactions, and printer driver operations. To estimate our model's parameters we use genetic algorithms. No assumption is made concerning the sequence of inks during printing, and the printers are treated as RGB devices (the printer-driver operations are included in the model). We have tested our characterization method, which requires only about 130 measurements to train the learning algorithm, on four different inkjet printers, using different kinds of paper and drivers. The test set used for model evaluation was composed of 777 samples, uniformly distributed over the RGB color space.