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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9027)

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.

Keywords

Chair design Generative design Fitness sharing Multimodel evolutionary algorithms 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ComputingImperial College LondonLondonUK

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