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Extracting Design Recommendations from Interactive Genetic Algorithm Experiments: Application to the Design of Sounds for Electric Vehicles

Tom Souaille 1, 2 Jean-François Petiot 1, 2 Mathieu Lagrange 1, 2 Nicolas Misdariis 3
2 SIMS - Signal, IMage et Son
LS2N - Laboratoire des Sciences du Numérique de Nantes
3 STMS-PDS - Perception et design sonores
STMS - Sciences et Technologies de la Musique et du Son
Abstract : The integration of users' perception in the design process is an 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.
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https://hal.archives-ouvertes.fr/hal-03312375
Contributor : Tom Souaille <>
Submitted on : Monday, August 2, 2021 - 12:06:10 PM
Last modification on : Tuesday, September 21, 2021 - 4:06:15 PM

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  • HAL Id : hal-03312375, version 1

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Tom Souaille, Jean-François Petiot, Mathieu Lagrange, Nicolas Misdariis. Extracting Design Recommendations from Interactive Genetic Algorithm Experiments: Application to the Design of Sounds for Electric Vehicles. ICED21 - 23rd International Conference on Engineering Design, Aug 2021, Gothenburg, Sweden. ⟨hal-03312375⟩

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