We propose adaptive rank tests for the location alternative in one sample, using as score function the percentile function of the Generalized Lambda Distribution (GLD ). We give expressions for its eciency as functions of the kurtosis parameters of the distribution used for the score function and those of the sampled distribution. A simulation study shows that the proposed tests maintain its nominal size and that this test using scores functions with small kurtosis parameter, are very ecient for samples coming from distributions with large kurtosis, overtaking the sign test and the Wilcoxon test. Reciprocally, tests which use scores from GLD  distributions with large kurtosis are more ecient when the sample comes from GLD  distributions with small kurtosis.