In our technique, the desired or intended image is considered the signal, and all other images are considered noise. The elicitation of the image is done by a biased selection of the objects that generate the greatest recognition, or signal to noise ratio, in each generation. In keeping with our example, consider evolving a face image. The first generation is pure noise, or in other words, all possible images are equally likely. At first, the probability of any image looking like a face, any face, is extremely unlikely.
Most images look like the snow on a TV tuned to a channel without a station.
However, one can select images whose pixels might give the impression of something rounded in the middle, dark in the area of hair, or light where cheeks may be. Since the images selected to parent the next generation have more of the signal and consequently less noise, it will give rise to a population of images whose mean signal to noise ratio will be greater than the previous generation. Eventually the signal to noise ratio is strong enough to elicit the intended image. In our evolutionary system one only sees the face that one seeks. The face is seen in different amounts of noise, from high to low.