Skip to main content
SpringerLink
Log in

Prototype Discovery Using Quality-Diversity

  • Conference paper
  • First Online:
Parallel Problem Solving from Nature – PPSN XV (PPSN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11101))

Included in the following conference series:

Abstract

An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions. Dimensionality reduction is used to define a similarity space, in which solutions are clustered into classes. These classes are represented by prototypes, which are presented to the user for selection. In the next iteration, quality-diversity focuses on searching within the selected class. A quantitative analysis is performed on a 2D airfoil, and a more complex 3D side view mirror domain shows how computer-aided ideation can help to enhance engineers’ intuition while allowing their design decisions to influence the design process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    DBSCAN’s parameterization is automated using the L Method [19].

  2. 2.

    http://web.mit.edu/drela/Public/web/xfoil/.

  3. 3.

    https://openfoam.org, simulation at 11 m/s.

  4. 4.

    Perplexity is set to 50, but at most half the number of samples.

References

  1. Balling, R.: Design by shopping: a new paradigm? In: Third World Congress of Structural and Multidisciplinary Optimization, pp. 295–297. ISSMO, New York (1999)

    Google Scholar 

  2. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “Nearest Neighbor” meaningful? In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-49257-7_15

    Chapter  Google Scholar 

  3. Bradner, E., Iorio, F., Davis, M.: Parameters tell the design story: ideation and abstraction in design optimization. In: Symposium on Simulation for Architecture & Urban Design, pp. 172–197. SCSI, San Diego (2014)

    Google Scholar 

  4. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland (1996)

    Google Scholar 

  5. Flager, F., Haymaker, J.: A comparison of multidisciplinary design, analysis and optimization processes in the building construction and aerospace industries. In: 24th W78 Conference on Bringing ITC Knowledge to Work, pp. 625–630. Elsevier, Maribor (2007)

    Google Scholar 

  6. Gaier, A., Asteroth, A., Mouret, J.: Data-efficient exploration, optimization, and modeling of diverse designs through surrogate-assisted illumination. In: Genetic and Evolutionary Computation Conference, pp. 99–106. ACM, Berlin (2017)

    Google Scholar 

  7. Heft, A.I., Indinger, T., Adams, N.A.: Introduction of a new realistic generic car model for aerodynamic investigations. SAE 2012 World Congress, Technical report. SAE, Detroit (2012)

    Google Scholar 

  8. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  Google Scholar 

  9. Hwang, C.L., Masud, A.S.M.: Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey, vol. 164. Springer, New York (1979). https://doi.org/10.1007/978-3-642-45511-7

    Book  MATH  Google Scholar 

  10. Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol. Comput. 1(2), 61–70 (2011)

    Article  Google Scholar 

  11. Lehman, J., Stanley, K.O.: Evolving a diversity of virtual creatures through novelty search and local competition. In: Genetic and Evolutionary Computation Conference, pp. 211–218. ACM, Dublin (2011)

    Google Scholar 

  12. van der Maaten, L., Hinton, G.: Visualizing data using T-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  13. Mouret, J.B., Clune, J.: Illuminating Search Spaces by Mapping Elites. arXiv:1504.04909 (2015)

  14. Niederreiter, H.: Low-discrepancy and low-dispersion sequences. J. Number Theory 30, 51–70 (1988)

    Article  MathSciNet  Google Scholar 

  15. Pearson, K.: On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901)

    Article  Google Scholar 

  16. Pugh, J.K., Soros, L.B., Stanley, K.O.: Quality diversity: a new frontier for evolutionary computation. Front. Robot. AI 3, 1–17 (2016)

    Article  Google Scholar 

  17. Rasmussen, C.E.: Gaussian Processes for Machine Learning. MIT press, Cambridge (2006)

    MATH  Google Scholar 

  18. Rosch, E.: Cognitive reference points. Cognit. Psychol. 7(4), 532–547 (1975)

    Article  Google Scholar 

  19. Salvador, S., Chan, P.: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. In: 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 576–584. IEEE, Boston (2003)

    Google Scholar 

  20. Schölkopf, B., Smola, A., Müller, K.-R.: Kernel principal component analysis. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 583–588. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0020217

    Chapter  Google Scholar 

  21. Shir, O.M., Bäck, T.: Niching in evolution strategies. In: 7th Annual Conference on Genetic and Evolutionary Computation, pp. 915–916. ACM, Washington (2005)

    Google Scholar 

  22. Singh, G., Deb, K.: Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In: 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1305–1312. ACM, Seattle (2006)

    Google Scholar 

  23. Stump, G.: Design space visualization and its application to a design by shopping paradigm. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 795–804. ASME, Chicago (2003)

    Google Scholar 

  24. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  25. Tomašev, N., Radovanović, M.: Clustering evaluation in high-dimensional data. In: Celebi, M.E., Aydin, K. (eds.) Unsupervised Learning Algorithms, pp. 71–107. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24211-8_4

    Chapter  Google Scholar 

  26. Wittgenstein, L.: Philosophische Untersuchungen. Basil Blackwell, Oxford (1953)

    MATH  Google Scholar 

  27. Zwicky, F.: Discovery, Invention Research Through the Morphological Approach. Macmillan, New York (1969)

    Google Scholar 

Download references

Acknowledgments

This work received funding from the German Federal Ministry of Education and Research (BMBF) under the Forschung an Fachhochschulen mit Unternehmen programme (grant agreement number 03FH012PX5 project “Aeromat”). The authors would like to thank Adam Gaier for their feedback.

Author information

Authors and Affiliations

  1. Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, Germany

    Alexander Hagg & Alexander Asteroth

  2. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Alexander Hagg & Thomas Bäck

Authors
  1. Alexander Hagg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Asteroth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Bäck
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Hagg .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hagg, A., Asteroth, A., Bäck, T. (2018). Prototype Discovery Using Quality-Diversity. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99253-2_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99252-5

  • Online ISBN: 978-3-319-99253-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation