Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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Hydraulic structures such as weirs or barrages are integral parts of diversion head works in the alluvial plains of India. However, there is no fixed procedure to design the basic barrage parameters. The depth of sheet piles, the length and thickness of floor may be treated as basic barrage parameters. Present work discusses the procedure of optimal design using Genetic algorithm (GA). An optimization based methodology is presented to obtain the optimum structural dimensions that minimize the total cost as well as satisfy the exit gradient criteria. Nonlinear optimization formulation (NLOF) with subsurface flow embedded as constraint in the optimization formulation is solved by GA. The results obtained in this study shows that considerable cost savings can be achieved when the proposed NLOF is solved using GA. Further, uncertainty in design, and hence cost from uncertain hydrogeologic parameter, seepage head, is investigated using fuzzy numbers.