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Retrieval of water optical properties and concentrations can be identified as a nonlinear optimization problem. This problem may be difficult to solve by conventional optimization methods owing to its multimodel nonconvex nature. This letter explores the potential of genetic algorithms as the optimization scheme in such a problem. A remote sensing reflectance model for optically deep waters was used to illustrate the performance of the algorithms. The superiority of genetic algorithms over conventional optimization methods was demonstrated by experiments on a field dataset.