Optimal evolutionary optimization hyper-parameters to mimic human user behavior

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Sneha Saha
  • Thiago Rios
  • Zhao Xu
  • Bernhard Sendhoff
  • Stefan Menzel

Colleges, School and Institutes

External organisations

  • Honda Research Institute Europe GmbH, Germany
  • NEC Laboratories Europe, Heidelberg, Germany
  • Southern University of Science and Technology, Shenzhen, China

Abstract

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.

Details

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Publication statusPublished - 20 Feb 2020
Event2019 IEEE Symposium Series on Computational Intelligence (SSCI) - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

NameIEEE Symposium Series on Computational Intelligence (SSCI)
PublisherIEEE

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence (SSCI)
CountryChina
CityXiamen
Period6/12/199/12/19

Keywords

  • clustering, evolutionary optimization, interactive designs, representations, similarity measure