Evolutionary optimization of logic-oriented systems

This study is concerned with an evolutionary methodology of designing logic-based models. These models dwell on a logic fabric of granular computing and learning capabilities of fuzzy neural networks. The proposed design comprises two fundamental phases, namely an evolutionary optimization (via Genetic Programming, GP) of the generic structure of the model that is followed by its parametric refinement completed in the form of a detailed gradient-based learning. We discuss the underlying algorithm and elaborate on the way in which GP helps cope with high dimensionality of the modeling problem (it is known that a significant number of variables leads to the failure of the parametric learning). The study is illustrated with the aid of a numeric example that provides a detailed insight into the performance of the logic-oriented models and quantifies crucial design issues.