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We use machine learning methods to control the spectral broadening experienced by femtosecond pulses in a highly nonlinear fiber. Combining a programmable spectral filter with a genetic algorithm or neural network allows us to optimize the nonlinear propagation dynamics to generate an on-demand target spectrum. Our approach is generic and can be adapted to a wide range of optical fibers and pump pulses. We expect our results to provide significant advances for adaptative control and tailored light sources.