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EXTRACTING DESIGN RECOMMENDATIONS FROM INTERACTIVE GENETIC ALGORITHM EXPERIMENTS: APPLICATION TO THE DESIGN OF SOUNDS FOR ELECTRIC VEHICLES

Published online by Cambridge University Press:  27 July 2021

Tom Souaille ,
Jean-François Petiot ,
Mathieu Lagrange  and
Nicolas Misdariis
Tom Souaille
Affiliation:
Ecole Centrale de Nantes, LS2N;
Jean-François Petiot*
Affiliation:
Ecole Centrale de Nantes, LS2N;
Mathieu Lagrange
Affiliation:
Ecole Centrale de Nantes, LS2N;
Nicolas Misdariis
Affiliation:
IRCAM
*
Petiot, Jean-François, Ecole Centrale de Nantes, LS2N, France, Jean-Francois.Petiot@ec-nantes.fr

Abstract

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The integration of users' perception in the design process is and important challenge for the optimization of products. This study describes how design recommendations can be drawn, from a perceptual experiment with a panel of subjects using a multi-objective interactive genetic algorithm (IGA). The application concerns the bi-objective optimization of the unpleasantness and the detectability of sounds for electric vehicles (EV). After a description of the experimental protocol for the assessment of the detectability and the unpleasantness of EV sounds (listening test), a set of optimal sounds (Pareto efficient) is defined with an IGA experiment. The analysis of these sounds, based on a probabilistic analysis of the selection process, leads to the definition of design recommendations. A second listening test, involving recommended sounds but also other design proposals, allows an evaluation of the validity of the approach. Results show that the sounds recommended obtain interesting performance, in particular to improve the detectability of EV sounds.

Keywords

OptimisationUser centred designParticipatory designsound designinteractiveoptimisation
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1567 - 1576
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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