Michael L. Mauldin
Genetic adaptive algorithms provide an efficient way to search large function spaces, and are increasingly being used in learning systems. One problem plaguing genetic learning algorithms is premature convergence, or convergence of the pool of active structures to a sub-optimal point in the space being searched. An improvement to the standard genetic adaptive algorithm is presented which guarantees diversity of the gene pool throughout the search. Maintaining genetic diversity is shown to improve off-line (or best) performance of these algorithms at the expense of poorer on-line (or average) performance, and to retard or prevent premature convergence.