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

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Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART 2015)

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

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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.

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References

  1. Dabbeeru, M.M., Mukerjee, A.: Discovering implicit constraints in design. Artif. Intell. Eng. Des. Anal. Manuf. 25, 57–75 (2011)

    Article  Google Scholar 

  2. Reed, K.: Aesthetic measures for evolutionary vase design. In: Machado, P., McDermott, J., Carballal, S. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 59–71. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  3. Schütze, O., et al.: On the detection of nearly optimal solutions in the context of single-objective space mission design problems. Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng. 225, 1229–1242 (2011)

    Article  Google Scholar 

  4. Deb, K., Saha, A.: Multimodal optimization using a bi-objective evolutionary algorithm. Evol. Comput. 20(1), 27–62 (2012). MIT Press

    Article  Google Scholar 

  5. Črepinšek, M., Liu, S.-H.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. 45, 3 (2013)

    Google Scholar 

  6. Xu, K., Zhang, H., Cohen-Or, D., Chen, B.: Fit and diverse: set evolution for inspiring 3D shape galleries. ACM Trans. Graph. 31(4), 57:1–57:10 (2012). Article 57

    Article  Google Scholar 

  7. Kalogerakis, E., Chaudhuri, S., Koller, D., Koltun, V.: A probabilistic model for component-based shape synthesis. ACM Trans. Graph. 31(4), 55:1–55:11 (2012). Article 55

    Article  Google Scholar 

  8. Clune J., Lipson H.: Evolving three-dimensional objects with a generative encoding inspired by developmental biology. In: Proceedings of the European Conference on Artificial Life, pp 141–148 (2011)

    Google Scholar 

  9. Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput. 9(1), 3–12 (2005). Springer

    Article  Google Scholar 

  10. Yüksel, A.Ç.: Automatic music generation using evolutionary algorithms and neural networks. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp 354–358 (2011)

    Google Scholar 

  11. Hsiao, S.-W., Tsai, H.-C.: Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. Int. J. Ind. Ergon. 35, 411–428 (2005)

    Article  Google Scholar 

  12. Trimble SketchUp. http://www.sketchup.com

  13. Mathworks Matlab. http://uk.mathworks.com/products/matlab/

  14. TU Delft DINED Database. http://dined.io.tudelft.nl/dined/full

  15. Winter, D.A.: Biomechanics and Motor Control of Human Movement, 4th edn. Wiley, New York (2009)

    Book  Google Scholar 

  16. SolidWorks Bearing Load Distribution. http://help.solidworks.com/2015/english/SolidWorks/cworks/c_Bearing_Load_Distribution.htm

  17. Todd, B.A.: Three-dimensional computer model of the human buttocks in vivo. J Rehabil. Res. Dev. 31(2), 111–119 (1994)

    Google Scholar 

  18. Zhu, H.: Modeling of pressure distribution of human body load on an office chair seat. Masters thesis. Department of Mechanical Engineering, Blekinge Institute of Technology (2013)

    Google Scholar 

  19. scikit-learn. http://scikit-learn.org/stable/index.html

  20. Breiman, L., Cutler, A.: Random forests. http://www.stat.berkeley.edu/ ~breiman/RandomForests/cc_home.htm

  21. Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Decision Engineering. Springer, London (2012)

    Google Scholar 

  22. Umetani, N., Igarashi, T., Mitra, N.J.: Guided exploration of physically valid shapes for furniture design. ACM Trans. Graph. 31(4), 86:1–86:11 (2012). Article 86

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computing, Imperial College London, South Kensington, London, SW7 2AZ, UK

    Kate Reed & Duncan F. Gillies

Authors
  1. Kate Reed
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  2. Duncan F. Gillies
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Corresponding author

Correspondence to Kate Reed .

Editor information

Editors and Affiliations

  1. University of Kent, Canterbury, United Kingdom

    Colin Johnson

  2. University of A Coruña, A Coruña, Spain

    Adrian Carballal

  3. University of Coimbra, Coimbra, Portugal

    João Correia

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© 2015 Springer International Publishing Switzerland

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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. https://doi.org/10.1007/978-3-319-16498-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-16498-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16497-7

  • Online ISBN: 978-3-319-16498-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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