  Complex systems' modeling and simulation are powerful ways to investigate a
multitude of natural phenomena providing extended knowledge on their structure
and behavior. However, enhanced modeling and simulation require integration of
various data and knowledge sources, models of various kinds (data-driven
models, numerical models, simulation models, etc.), intelligent components in
one composite solution. Growing complexity of such composite model leads to the
need of specific approaches for management of such model. This need extends
where the model itself becomes a complex system. One of the important aspects
of complex model management is dealing with the uncertainty of various kinds
(context, parametric, structural, input/output) to control the model. In the
situation where a system being modeled, or modeling requirements change over
time, specific methods and tools are needed to make modeling and application
procedures (meta-modeling operations) in an automatic manner. To support
automatic building and management of complex models we propose a general
evolutionary computation approach which enables managing of complexity and
uncertainty of various kinds. The approach is based on an evolutionary
investigation of model phase space to identify the best model's structure and
parameters. Examples of different areas (healthcare, hydrometeorology, social
network analysis) were elaborated with the proposed approach and solutions.
