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The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. An important problem in MFL nondestructive evaluation (NDE) is signal inverse problem, wherein the defect profile and its parameters are determined using the information contained in the measured signals. This paper proposes a genetic-algorithm-based inverse algorithm for reconstructing 2-D defect from MFL signals. In the algorithm, a radial basis function neural network (RBFNN) is used as forward model, and genetic algorithm is used to solve the optimization problem in the inverse problem. Experimental results are presented to demonstrate the effectiveness of the proposed inverse algorithm.