Hybridization Schemes in Architectures of Computational Intelligence

Abstract

While the essence of Computational Intelligence hinges profoundly on the symbiotic use of their underlying technologies (viz. neurocomputing, granular computing, and predominantly fuzzy sets, and evolutionary optimization), there are several other equally promising development avenues where a hybrid usage of the underlying technologies is worth pursuing. In this study, we concentrate on the hybrid concepts and constructs available within the realm of Granular Computing (GC). Given the highly diversified landscape of GC, we discuss main directions of forming hybrid structures involving individual technologies of information granulation, elaborate on the fundamental communication, interoperability, and orthogonality issues and propose some general ways of building hybrid constructs of GC which are of immediate interest to system modeling realized in the realm of Computational Intelligence. We also shed light on the central role of the concepts of information granularity, information granules and ensuing hybrid constructs. Furthermore we emphasize a role of hierarchical modeling that is directly supported by stratified aspect of information granules formed at nested levels of specificity. The central issue of human-centricity of such models is also highlighted.