Using state-of-the-art computational techniques, a genetic algorithm (GA) and an accuracy-based learning classifier system (XCS) were shown to produce optimal operational solutions for gate structures in irrigation canals. An XCS successfully developed a set of operational rules for canal gates through the exploration and exploitation of rules using a GA, with the support of an unsteady-state hydraulic simulation model. A computer program which implemented the XCS was used to develop operational rules to operate all canal gate structures simultaneously, while maintaining water depth near target values during variable-demand periods, and with a hydraulically stabilized system when demands no longer changed. This model can be applied to canal networks with constant or variable demands within the limits of current hydraulic simulation capabilities. The program output is a set of feasible and optimal operating rules for multiple gate structures, facilitating the automation of open-channel irrigation conveyance systems. Results from sample applications of this technique are presented in the companion paper.