Schema processing under proportional selection in the presence of random effects

Traditional selection in genetic algorithms has relied on reproduction in proportion to observed fitness. This method of selection devotes samples to the observed schemata in a form described by the well known schema theorem. When schema fitness takes the form of a random variable, however, the expected number of samples from extant schemata may not be described by the schema theorem and varies according to the specific random variables involved.