Multi-Objective Cultural Algorithms

Within a cultural context we constantly deal effectively with multiple objectives. A computational version of cultural systems, Cultural Algorithms, has been extended to deal with multi-objective optimization problems. These approaches while employing the basic framework have used only a subset of the available knowledge sources. In this paper we present an extension of Cultural Algorithms for Multi-Objective optimization, MOCAT, the fully utilizes all of the available categories of knowledge sources. The synergy of this ensemble is demonstrated through the application to an example problem and the results compared with that of other approaches in metric terms.