Hybrid inductive models: deterministic crowding employed

Our research draws on experience with group method of data handling (GMDH) introduced by Ivachknenko in 1966. We have modified multilayered iterative algorithm (MIA) that is commonly used to generate inductive models of real-world systems. In our algorithm, heterogeneous units are used instead of units with given polynomial transfer function and therefore hybrid inductive models (HIMs) are generated. This work shows how to improve the efficiency of search for optimal HIMs. This is attained by employing deterministic crowding (DC) method proposed by Mahfoud in 1995. As a by-product of using the DC method, we can estimate the importance of input variables for modeled output (sensitivity analysis).