Evolving Diverse Design Populations Using Fitness Sharing and Random Forest Based Fitness Approximation
Inhaltsverzeichnis
Referenz
Kate Reed, Duncan F. Gillies: Evolving Diverse Design Populations Using Fitness Sharing and Random Forest Based Fitness Approximation. In: EvoMUSART 2015, 187-199.
DOI
http://link.springer.com/chapter/10.1007/978-3-319-16498-4_17
Abstract
A large, diverse design space will contain many non-viable designs. To locate the viable designs we need to have a method of testing the designs and a way to navigate the space. We have shown that using machine learning on artificial data can accurately predict the viability of chairs based on a range of ergonomic considerations. We have also shown that the design space can be explored using an evolutionary algorithm with the predicted viability as a fitness function. We find that this method in conjunction with a fitness sharing technique can maintain a diverse population with many potential viable designs.
Extended Abstract
Bibtex
@incollection{
year={2015},
isbn={978-3-319-16497-7},
booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
volume={9027},
series={Lecture Notes in Computer Science},
editor={Johnson, Colin and Carballal, Adrian and Correia, João},
doi={10.1007/978-3-319-16498-4_17},
title={Evolving Diverse Design Populations Using Fitness Sharing and Random Forest Based Fitness Approximation},
url={http://dx.doi.org/10.1007/978-3-319-16498-4_17 http://de.evo-art.org/index.php?title=Evolving_Diverse_Design_Populations_Using_Fitness_Sharing_and_Random_Forest_Based_Fitness_Approximation },
publisher={Springer International Publishing},
keywords={Chair design; Generative design; Fitness sharing; Multimodel evolutionary algorithms},
author={Reed, Kate and Gillies, DuncanF.},
pages={187-199},
language={English}
}
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