Fashion Design Aid System with Application of Interactive Genetic Algorithms
Inhaltsverzeichnis
Referenz
Nazanin Alsadat Tabatabaei Anaraki: Fashion Design Aid System with Application of Interactive Genetic Algorithms. In: EvoMUSART 2017, 289-303.
DOI
https://doi.org/10.1007/978-3-319-55750-2_20
Abstract
These days, consumers can make their choice from a wide variety of clothes provided in the market; however, some prefer to have their clothes custom-made. Since most of these consumers are not professional designers, they contact a designer to help them with the process. This approach, however, is not efficient in terms of time and cost and it does not reflect the consumer’s personal taste as much as desired. This study proposes a design system using Interactive Genetic Algorithm (IGA) to overcome these problems. IGA differs from traditional Genetic Algorithm (GA) by leaving the fitness function to the personal preference of the user. The proposed system uses user’s taste as a fitness value to create a large number of design options, and it is based on an encoding scheme either describing a dress as a whole or as a two-part piece of clothing. The system is designed in the Rhinoceros 3D software, using python, which provides good speed and interface options. The assessment experiments with several subjects indicated that the proposed system is effective.
Extended Abstract
Bibtex
@incollection{
year={2017},
isbn={978-3-319-55750-2},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={10198},
series={Lecture Notes in Computer Science},
editor={Correia, João and Ciesielski, Vic and Liapis, Antonios},
doi={10.1007/978-3-319-55750-2_20},
title={Fashion Design Aid System with Application of Interactive Genetic Algorithms},
url={https://link.springer.com/chapter/10.1007/978-3-319-55750-2_20 http://de.evo-art.org/index.php?title=Fashion_Design_Aid_System_with_Application_of_Interactive_Genetic_Algorithms},
publisher={Springer International Publishing},
keywords={Fashion design; Interactive genetic algorithm; Artificial evolution; Human-computer interface },
author={Anaraki, Nazanin Alsadat Tabatabaei},
pages={289-303},
language={English}
}
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