Evolutionary simulation of complex networks' structures with specific functional properties

Kashirin, Victor V.; Lantseva, Anastasia A.; Ivanov, Sergey V.; Kovalchuk, Sergey V.; Boukhanovsky, Alexander V.

doi:10.1016/j.jal.2016.11.012

Cited by 3 publications

(3 citation statements)

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“…The proposed approach is based on several key ideas, aimed to extend uncertainty management in complex system modeling and simulation. 1) Disjoint consideration of model, data, and system in terms of structure, behavior, and quality is aimed toward a system-level review of modeling and simulation process and distinguish between the uncertainty of various kinds originated from different level [9].…”

Section: Conceptual Basismentioning

confidence: 99%

“…3) EC approaches are widespread in modeling and simulation of complex systems [9,10]. We believe that systematization of this process with separate consideration of spaces for a system (with its sub-systems) and a model (with its sub-models) could enhance such solutions significantly.…”

Section: Conceptual Basismentioning

confidence: 99%

“…1) Disjoint consideration of model, data, and system in terms of structure, behavior, and quality is aimed toward a system-level review of modeling and simulation process and distinguish between the uncertainty of various kinds originated from different level [9].…”

Section: Conceptual Basismentioning

confidence: 99%

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A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification

et al. 2018

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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.

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Section: Conceptual Basismentioning

confidence: 99%

Section: Conceptual Basismentioning

confidence: 99%