Through the use of evolutionary computation, empirical models for chemical processes can be evolved that are more cost-effective than models determined by means of classical statistical techniques. These strategies do not require explicit specification of a model structure, but explore candidate models assembled from sets of variables, parameters and simple mathematical operators. The application of the proposed strategies is illustrated by means of three examples, two of which are based on data pertaining to leaching experiments. Since the evolved models were derived from terminal sets containing only the most basic operators, their structures tended to be complicated, making for less easy interpretation, similar to neural networks and other non-parametric models. Nonetheless, the evolved models were either of comparable accuracy or significantly more accurate than those which were previously developed by means of standard least-squares methods.