Probabilistic Decision Making for Interactive Evolution with Sensitivity Analysis

  • Jonathan Eisenmann
  • Matthew Lewis
  • Rick Parent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8601)


Recent research in the area of evolutionary algorithms and interactive design tools for ideation has investigated how sensitivity analysis can be used to enable region-of-interest selection on design candidates. Even though it provides more precise control over the evolutionary search to the designer, the existing methodology for this enhancement to evolutionary algorithms does not make full use of the information provided by sensitivity analysis and may lead to premature convergence. In this paper, we describe the shortcomings of previous research on this topic and introduce an approach that mitigates the problem of early convergence. A discussion of the trade-offs of different approaches to sensitivity analysis is provided as well as a demonstration of this new technique on a parametric model built for character design ideation.


interactive evolution sensitivity analysis probabilistic genetic operators 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Avila, S.L., Lisboa, A.C., Krahenbuhl, L., Carpes, W.P., Vasconcelos, J.A., Saldanha, R.R., Takahashi, R.H.C.: Sensitivity analysis applied to decision making in multiobjective evolutionary optimization. IEEE Transactions on Magnetics 42(4), 1103–1106 (2006), CrossRefGoogle Scholar
  2. 2.
    Dawkins, R.: The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design. Norton (1986),
  3. 3.
    Eisenmann, J., Lewis, M., Parent, R.: Inverse Mapping with Sensitivity Analysis for Partial Selection in Interactive Evolution. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 72–84. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Erhan, H., Woodbury, R., Salmasi, N.H.: Visual sensitivity analysis of parametric design models: improving agility in design. Master’s thesis, School of Interactive Arts and Technology - Simon Fraser University (2009)Google Scholar
  5. 5.
    Herman, J.D.: SALib (October 2013),
  6. 6.
    Herman, J.D., Kollat, J.B., Reed, P.M., Wagener, T.: Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models. Hydrology and Earth System Sciences Discussions 10(4), 4275–4299 (2013), CrossRefGoogle Scholar
  7. 7.
    Joe, S., Kuo, F.Y.: Constructing Sobol Sequences with Better Two-Dimensional Projections. SIAM J. Sci. Comput. 30(5), 2635–2654 (2008), CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Kim, V.G., Li, W., Mitra, N.J., DiVerdi, S., Funkhouser, T.: Exploring collections of 3D models using fuzzy correspondences. ACM Trans. Graph. 31(4) (July 2012),
  9. 9.
    Lee, J.H., Kim, H.S., Cho, S.B.: Accelerating evolution by direct manipulation for interactive fashion design. In: Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2001, pp. 343–347. IEEE (2001),
  10. 10.
    Lewis, M.: Evolutionary Visual Art and Design. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 3–37. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Morris, M.D.: Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics 33(2), 161–174 (1991), CrossRefGoogle Scholar
  12. 12.
    Parmee, I.C., Cvetković, D.C., Watson, A.H., Bonham, C.R.: Multiobjective Satisfaction within an Interactive Evolutionary Design Environment. Evol. Comput. 8(2), 197–222 (2000), CrossRefGoogle Scholar
  13. 13.
    Perlin, K.: Improving noise. ACM Trans. Graph. 21(3), 681–682 (2002), CrossRefGoogle Scholar
  14. 14.
    Saltelli, A., Chan, K.: Scott: Sensitivity analysis. J. Wiley & Sons. (2000),
  15. 15.
    Semet, Y.: Interactive Evolutionary Computation: a survey of existing theory (2002),
  16. 16.
    Shan, S., Wang, G.G.: Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Structural and Multidisciplinary Optimization 41(2), 219–241 (2010), CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Side Effects Software: HOUDINI FX. HOUDINI (2013),
  18. 18.
    Sims, K.: Artificial evolution for computer graphics. In: SIGGRAPH 1991 Proceedings, vol. 25, pp. 319–328. ACM, New York (1991), Google Scholar
  19. 19.
    Sobol, I.M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 55(1-3), 271–280 (2001), CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Takagi, H., Kishi, K.: On-line knowledge embedding for an interactive EC-based montage system, pp. 280–283 (December 1999),
  21. 21.
    Takagi, H.: New IEC Research and Frameworks Aspects of Soft Computing, Intelligent Robotics and Control. In: Fodor, J., Kacprzyk, J. (eds.) Aspects of Soft Computing, Intelligent Robotics and Control. SCI, vol. 241, pp. 65–76. Springer, Heidelberg (2009), CrossRefGoogle Scholar
  22. 22.
    Todd, S., Latham, W.: Evolutionary art and computers. Academic Press (1992),
  23. 23.
    Umetani, N., Igarashi, T., Mitra, N.J.: Guided Exploration of Physically Valid Shapes for Furniture Design. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2012) 31(4) (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jonathan Eisenmann
    • 1
    • 2
  • Matthew Lewis
    • 2
  • Rick Parent
    • 1
  1. 1.Department of Computer Science and EngineeringThe Ohio State UniversityColumbusUSA
  2. 2.The Advanced Computing Center for the Arts and Design (ACCAD)The Ohio State UniversityColumbusUSA

Personalised recommendations