Many optimization problems are multi-modal. In certain cases, we are interested in finding multiple locally optimal solutions rather than just a single optimum as is computed by traditional genetic algorithms (GAs). Several niching techniques have been developed that seek to find multiple such local optima. These techniques, which include sharing and crowding, are clearly powerful and useful. But they do not explicitly let the user control the number of local optima being computed, which we believe to be an important capability.

In this paper, we develop a method that provides, as an input parameter to niching, the desired number of local optima. Our method integrates techniques from feedback control, includes a sensor based on clustering, and utilizes a scaling parameter in Generalized Crowding to control the number of niches being explored. The resulting Feedback Control GA (FCGA) is tested in several experiments and found to perform well compared to previous approaches. Overall, the integration of feedback control and Generalized Crowding is shown to effectively guide the search for multiple local optima in a more controlled fashion. We believe this novel capability has the potential to impact future applications as well as other evolutionary algorithms.