This study developed a simulation/optimization model based on artificial 
neural networks and genetic algorithm techniques for solving conjunctive 
use of groundwater problems. The model simultaneously addresses all 
significant flows in a dynamic hydraulically connected stream-aquifer 
system. It also addresses multiple objectives. The genetic algorithm is 
a search procedure based on the mechanics of natural selection and 
genetics. It has been applied almost exclusively to single objective 
optimization problems. Water resources projects are generally constructed 
to serve multiple objectives. The nondominated sorting genetic algorithm 
was adopted for multiobjective optimization. The artificial neural network 
was used to represent simulation constraints inside the optimization model. 
It predicted groundwater system responses to changes in decision variables. 
The linked ANNGA model was able to solve complicated nonlinear reservoir-
stream-aquifer system equations. A fuzzy-penalty function method was used 
to implicitly handle constraints on state variables. Three conflicting
objectives were considered: (1) maximization of the sum of unsteady 
groundwater pumping and surface water diversions for three consecutive 
60-day periods, (2) minimization of operating costs, and (3) maximization of 
hydropower production. The three objective functions were constrained by 
21 state variables. Water use from one multipurpose water reservoir, two 
pumping wells, two stream diversions, and one reservoir diversion was 
optimized. Three scenarios were investigated. The first scenario optimized 
the first two objectives simultaneously. The second scenario maximized total 
water diverted and minimized pumping costs concurrently. The third scenario 
was a combination of the first two and optimized the three objective functions. 
The genetic algorithm control-parameters were investigated. A crossover 
probability of 0.6 and a niche size corresponding to 200 peaks were most 
appropriate for all tested scenarios. Mahfoud's population sizing method was 
investigated and validated. Population sizes for the three scenarios were
obtained accordingly. The 21 artificial neural networks estimated state 
variables within a linear correlation range of 0.85 and 0.99. The final 
product of all three scenarios was a set of trade-off curves that allow the 
manager to assess the relative importance of each objective. The new
simulation/optimization model was robustly able to simulate water flows and 
optimize different water management problems without violating any of the 
specified constraints.