Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior

Saha, Sneha1; Rios, Thiago1; Minku, Leandro L.2; Yao, Xin2; Xu, Zhao3; Sendhoff, Bernhard1; Menzel, Stefan1

10.1109/SSCI44817.2019.9002958

2019

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

858-866

6-9 Dec. 2019

Xiamen, China

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

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.
© 2019 IEEE.

evolutionary optimization similarity measure representations clustering interactive designs

其他

英语

EI ; CPCI-S

[766186]

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467200121

20201108276664

Artificial intelligence ; Deformation ; Product design ; Shape optimization ; User centered design ; User experience

Artificial Intelligence:723.4 ; Production Engineering:913.1 ; Optimization Techniques:921.5 ; Social Sciences:971

EV Compendex

1.Honda Research Institute Europe GmbH, Offenbach; 63073, Germany
2.University of Birmingham, CERCIA, School of Computer Science, United Kingdom
3.Southern University of Science and Technology, Department of Computer Science and Engineering, Shenzhen, China

Saha, Sneha,Rios, Thiago,Minku, Leandro L.,et al. Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:858-866.