Wavelet transforms have proved to be very powerful tools for image compression, since many state-of-the-art image codecs employ DWT into their algorithms. One advantage of this transform is the provision of both frequency and spatial localization of image energy compacted into a small fraction of the transform coefficients, equally likely to be positive or negative. Previous studies have verified that there is a strong correlation between the sign of a wavelet coefficient and the signs of their neighbors. This correlation opens the possibility of using a sign predictor in order to improve the image compression process. In this work we evaluate two algorithms, one based on Genetic programming and other based on Simulated Annealing process in order to obtain a good wavelet sign predictor.