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Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior

Shape morphing methods are a key representation in human user-centered design as well as computational optimization of engineering applications in the automotive domain.3D digital objects are modified using deformation algorithms to alter the shape for optimal product performance or design aesthetics. We imagine a system which can learn from historic user deformation sequences and support the user in present design tasks by predicting potential design variations based on currently observed design changes carried out by the user. Towards a practical realization, a large amount of human user deformation sequence data is required which is practically not available. To overcome this limitation, we propose to use a computational target shape matching optimization whose hyper-parameters are tuned to exemplary human user sequence data and that allows us to afterwards generate large data-sets of human-like shape modification data in an automated fashion. In addition, we classified the user sequences to experience levels based on their variance. These user experience-tuned evolutionary optimizers allow us in future to mimic different user behavior and generate a large number of potential design variations in an automated fashion.