Learning and adaptation occur in evolutionary algorithms because populations of individuals (models) adapt with time. Obviously, the larger the population the faster adaptation. But there’s a caveat here: in a computer, the higher the number of models the longer it takes to process them. So one must choose a population size that enables an efficient adaptation in record time. In Gene Expression Programming, the learning algorithm used by APS, this can be accomplished well enough with populations of 30-200 individuals provided that the right degree of genetic modification is being introduced in the evolving populations. How this is accomplished is explained in the next section.