Skip to main content

Summary and Outlook

  • Chapter
  • First Online:
Book cover Machine Learning for Evolution Strategies

Part of the book series: Studies in Big Data ((SBD,volume 20))

  • 3989 Accesses

Abstract

ES are famous blackbox optimization algorithms. The variants with Gaussian mutation are tailored to continuous optimization problems.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Glasmachers, T., Igel, C.: Maximum likelihood model selection for 1-norm soft margin svms with multiple parameters. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1522–1528 (2010)

    Article  Google Scholar 

  2. Stoean, C. Stoean, R.: Support Vector Machines and Evolutionary Algorithms for Classification—Single or Together?, volume 69 of Intelligent Systems Reference Library Springer (2014)

    Google Scholar 

  3. Oehmcke, S., Heinermann, J., Kramer, O.: Analysis of diversity methods for evolutionary multi-objective ensemble classifiers. In: Proceedings of the 18th European Conference on Applications of Evolutionary Computation, EvoApplications 2015, pp. 567–578. Copenhagen, Denmark, 8–10 April 2015

    Google Scholar 

  4. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In: Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, PPSN VI 2000, pp. 849–858. Paris, France, 18–20 Sept 2000

    Google Scholar 

  5. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello, C.A.C.: A survey of multiobjective evolutionary algorithms for data mining: part I. IEEE Trans. Evol. Comput. 18(1), 4–19 (2014)

    Article  Google Scholar 

  6. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello, C.A.C.: Survey of multiobjective evolutionary algorithms for data mining: part II. IEEE Trans. Evol. Comput. 18(1), 20–35 (2014)

    Article  Google Scholar 

  7. Treiber, N.A., Kramer, O.: Evolutionary feature weighting for wind power prediction with nearest neighbor regression. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2015, pp. 332–337. Sendai, Japan, 25–28 May 2015

    Google Scholar 

  8. Kramer, O.: A particle swarm embedding algorithm for nonlinear dimensionality reduction. In: Proceedings of the 8th International Conference on Swarm Intelligence, ANTS 2012, pp. 1–12. Brussels, Belgium, 12–14 Sept 2012

    Google Scholar 

  9. Kramer, O.: Dimensionality Reduction with Unsupervised Nearest Neighbors, volume 51 of Intelligent Systems Reference Library. Springer (2013)

    Google Scholar 

  10. Kramer, O.: Hybrid manifold clustering with evolutionary tuning. In: Proceedings of the 18th European Conference on Applications of Evolutionary Computation, EvoApplications 2015, pp. 481–490. Copenhagen, Denmark (2015)

    Google Scholar 

  11. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth (1984)

    Google Scholar 

  12. Lückehe, D., Kramer, O.: Leaving local optima in unsupervised kernel regression. In: Proceedings of the 24th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2014, pp. 137–144. Hamburg, Germany, 15–19 Sept 2014

    Google Scholar 

  13. Kramer, O.: Supervised manifold learning with incremental stochastic embeddings. In: Proceedings of the 23rd European Symposium on Artificial Neural Networks, ESANN 2015, pp. 243–248. Bruges, Belgium (2015)

    Google Scholar 

  14. Meinicke, P., Klanke, S., Memisevic, R., Ritter, H.: Principal surfaces from unsupervised kernel regression. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1379–1391 (2005)

    Article  Google Scholar 

  15. Klanke, S., Ritter, H.: Variants of unsupervised kernel regression: general cost functions. Neurocomputing 70(7–9), 1289–1303 (2007)

    Article  Google Scholar 

  16. Kramer, O.: On evolutionary approaches to unsupervised nearest neighbor regression. In: Proceedings of the Applications of Evolutionary Computation—EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, pp. 346–355. Málaga, Spain, 11–13 April 2012

    Google Scholar 

  17. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Deng, L., Yu, D.: Deep learning: Methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oliver Kramer .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kramer, O. (2016). Summary and Outlook. In: Machine Learning for Evolution Strategies. Studies in Big Data, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-33383-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33383-0_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33381-6

  • Online ISBN: 978-3-319-33383-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics