Abstract
A genetic algorithm can be effectively applied to the problem of optimizing the operation of a river/reservoir system for maximum economic return. Solution of such problems requires both a detailed model of the system and a powerful optimization technique. Standard approaches often represent a trade-off between model accuracy and optimization capability. When a large system is modeled in detail, optimization techniques such as dynamic programming may prove intractable. Alternately, techniques such as linear programming may not allow accurate system modelling. A genetic algorithm approach allows the use of an accurate system model while retaining powerful search capabilities. The effectiveness of this approach is demonstrated by the results of its application to a complex hydraulic/economic problem based on the Rio Grande Project in southern New Mexico.
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