A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification
Sergey V. Kovalchuk, Oleg G. Metsker, Anastasia A. Funkner, Ilia O., Kisliakovskii, Nikolay O. Nikitin, Anna V. Kalyuzhnaya, Danila A. Vaganov,, Klavdiya O. Bochenina

TL;DR
This paper introduces a general evolutionary computation approach for managing complex models with uncertainty, enabling automatic building and adaptation across various domains like healthcare and hydrometeorology.
Contribution
It proposes a novel evolutionary investigation method for complex model management, addressing uncertainty and adaptability in dynamic systems.
Findings
Effective management of model complexity and uncertainty.
Successful application in healthcare, hydrometeorology, and social network analysis.
Automatic model structure and parameter identification.
Abstract
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…
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