Nothing Special   »   [go: up one dir, main page]

Skip to main content

When Novelty Is Not Enough

  • Conference paper
Applications of Evolutionary Computation (EvoApplications 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6624))

Included in the following conference series:

Abstract

The idea of evolving novel rather than fit solutions has recently been offered as a way to automatically discover the kind of complex solutions that exhibit truly intelligent behavior. So far, novelty search has only been studied in the context of problems where the number of possible “different” solutions has been limited. In this paper, we show, using a task with a much larger solution space, that selecting for novelty alone does not offer an advantage over fitness-based selection. In addition, we examine how the idea of novelty search can be used to sustain diversity and improve the performance of standard, fitness-based search.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. thesis, The University of Michigan, Ann Arbor, MI (1975), university Microfilms No. 76-09381

    Google Scholar 

  2. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)

    Book  MATH  Google Scholar 

  3. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms, pp. 148–154. Morgan Kaufmann, San Francisco (1987)

    Google Scholar 

  4. Gomez, F.: Sustaining diversity using behavioral information distance. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 113–120 (2009)

    Google Scholar 

  5. Krause, A., Guestrin, C.: Nonmyopic active learning of gaussian processes: An exploration- exploitation approach. In: Proceedings of the International Conference on Machine Learning (2007)

    Google Scholar 

  6. Lehman, J., Stanley, K.: Abandoning objectives: Evolution through the search for novelty alone. To appear in: Evolutionary Computation Journal (2010)

    Google Scholar 

  7. 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, Cambridge (2008)

    Google Scholar 

  8. Lehman, J., Stanley, K.O.: Efficiently evolving programs through the search for novelty. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010 (2010)

    Google Scholar 

  9. Lehman, J., Stanley, K.O.: Revising the evolutionary computation abstraction: minimal criteria novelty search. In: Proceedings of the Genetic and Evolutionary Computation (GECCO 2010), pp. 103–110. ACM, New York (2010)

    Google Scholar 

  10. MacKay, D.J.C.: Information-based objective functions for active data selection. neural computation. Neural Computation 4, 550–604 (1992)

    Google Scholar 

  11. Risi, S., Vanderbleek, S.D., Hughes, C.E., Stanley, K.O.: How novelty search escapes the deceptive trap of learning to learn. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009), pp. 153–160. ACM, New York (2009)

    Google Scholar 

  12. Schmidhuber, J.: Curious model-building control systems. In: Proceedings of the International Joint Conference on Neural Networks, Singapore, vol. 2, pp. 1458–1463. IEEE press, Los Alamitos (1991)

    Google Scholar 

  13. Schmidhuber, J.: Developmental robotics, optimal articial curiosity, creativity, music, and the ne arts. Connection Science 18, 173–187 (2006)

    Article  Google Scholar 

  14. Teller, A.: Advances in Genetic Programming, ch. 9. MIT Press, Cambridge (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cuccu, G., Gomez, F. (2011). When Novelty Is Not Enough. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20525-5_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20524-8

  • Online ISBN: 978-3-642-20525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics