Towards Directed Open-Ended Search by a Novelty Guided Evolution Strategy

  • Lars Graening
  • Nikola Aulig
  • Markus Olhofer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)


In the conceptional phases of design optimization tasks it is required to find new innovative solutions to a given problem. Although evolutionary algorithms are suitable methods to this problem, the search of a wide range of the solution space in order to identify novel concepts is mainly driven by random processes and is therefore a demanding task, especially for high dimensional problems. To improve the exploration of the design space additional criteria are proposed in the presented work which do not evaluate solely the quality of a solution but give an estimation of the probability to find alternative optima. To realize these criteria, concepts of novelty and interestingness are employed. Experiments on test functions show that these novelty guided evolution strategies identify multiple optima and demonstrate a switching between states of exploration and exploitation. With this we are able to provide first steps towards an alternative search algorithm for multi-modal functions and the search during conceptual design phases.


Evolutionary algorithm open-endedness interestingness multi-objective optimization novelty detection prediction error niching 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bentley, P.J., Corne, D.W. (eds.): Creative Evolutionary Systems. Morgan Kaufmann, San Francisco (2001)Google Scholar
  2. 2.
    Shir, O.M., Bäck, T.: Niching in evolution strategies. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 915–916. ACM, New York (2005)CrossRefGoogle Scholar
  3. 3.
    Shir, O.M.: Dynamic niching in evolution strategies with covariance matrix adaptation. In: Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005, Piscataway, pp. 2584–2591. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  4. 4.
    Shir, O.M., Bäck, T.: Niche radius adaptation in the cma-es niching algorithm. In: Parallel Problem Solving from Nature (PPSN IX), Reykjavik, Iceland (2006)Google Scholar
  5. 5.
    Herdy, M.: Evolution strategies with subjective selection. In: PPSN IV: Proceedings of the 4th International Conference on Parallel Problem Solving from Nature, London, UK, pp. 22–31. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  6. 6.
    Takagi, H.: Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE 89, 1275–1290 (2001)CrossRefGoogle Scholar
  7. 7.
    Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 8, 970–974 (1996)CrossRefGoogle Scholar
  8. 8.
    Schmidhuber, J.: What’s interesting? Idsia-35-97, IDSIA, Switzerland (1997)Google Scholar
  9. 9.
    Saunders, R.: Curious Design Agents and Artificial Creativity. PhD thesis, Faculty of Architecture, The University of Sydney (2001)Google Scholar
  10. 10.
    Risi, S., Vanderbleek, S.D., Hughes, C.E., Stanley, K.O.: How novelty search escapes the deceptive trap of learning to learn. In: GECCO 2009: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 153–160. ACM, New York (2009)CrossRefGoogle Scholar
  11. 11.
    Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference on Artificial Life (ALIFE XI). MIT Press, Cambrige (2008)Google Scholar
  12. 12.
    Oudeyer, P.Y., Kaplan, F.: What is intrinsic motivation? a typology of computational approaches. Frontiers in Neurorobotics (2007)Google Scholar
  13. 13.
    Bishop, C.M.: Novelty detection and neural network validation. In: Proc. IEE Conference on Vision and Image Signal Processing, pp. 217–222 (1994)Google Scholar
  14. 14.
    Igel, C., Husken, M.: Improving the rprop learning algorithm. In: Second International Symposium on Neural Computation, pp. 115–121 (2000)Google Scholar
  15. 15.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lars Graening
    • 1
  • Nikola Aulig
    • 2
  • Markus Olhofer
    • 1
  1. 1.Honda Research Institute Europe GmbHOffenbach/MainGermany
  2. 2.Technical University DarmstadtDarmstadt

Personalised recommendations