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Probing the physical regions in large parameter spaces of typical Standard Model (SM) extensions can be a very difficult computational task. In this thesis project, a new framework has been developed that utilises well-known Machine Learning (ML) techniques in the form of neural networks trained by a genetic algorithm. This framework is rather generic and designed to explore new physics model para
