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Evolving Diverse Design Populations Using Fitness Sharing and Random Forest Based Fitness Approximation

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9027)


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.


  • Chair design
  • Generative design
  • Fitness sharing
  • Multimodel evolutionary algorithms

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  • DOI: 10.1007/978-3-319-16498-4_17
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Reed, K., Gillies, D.F. (2015). Evolving Diverse Design Populations Using Fitness Sharing and Random Forest Based Fitness Approximation. In: Johnson, C., Carballal, A., Correia, J. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2015. Lecture Notes in Computer Science(), vol 9027. Springer, Cham.

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