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Assisted by soft computing methods, the work we present in this paper focuses on the design of energy-efficient algorithms for binaural hearing aids that aim to separate speech from other sounds the hearing impaired person is not interested in. To do this, the right and left hearing aids need to wirelessly transmit to each other some parameters involved in the speech separation algorithm. The problem is that this transmission appreciably reduces the battery life, the most important constrain for designing advanced algorithms in hearing aids. Reducing the number of bits used to represent the parameters to be transmitted will bring down the power consumption, but at the expense of degrading the ability of the system to separate the speech from the other sound sources. Aiming at solving this problem, our approach, based on quantizing the parameters to be transmitted, basically consists in computing the adequate number of quantization bits by means of a combination of neural networks and genetic algorithms in the effort of finding a balance between low bit rate (and thus, low power consumption) and good separation of speech. The results show that even by using only 2 bits/quantized-sample, the quality of the separation is as high as 70% of the limiting non-quantized quality separation factor, which has been found to be 85%.