Skip to main content

Evaluation of Search Performance of Evolutionary Computation by Transfer Entropy

  • Conference paper
  • First Online:
  • 298 Accesses

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 12))

Abstract

In Evolutionary Computation, the search space made from genotype and the search space made from phenotype is usually quite different. This study tries to reveal the relation among genotype space, phenotype space, and fitness landscape using transfer entropy. The preliminary experiment shows a promising result.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  1. Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  2. Mengshoel, O.J., Goldberg, D.E.: The crowding approach to niching in genetic algorithms. Evol. Comput. 16(3), 315–354 (2008)

    Article  Google Scholar 

  3. Mori, N., Yoshida, J., Tamaki, H., Nishikawa, H.: A thermodynamical selection rule for the genetic algorithm. In: Proceedings of 1995 IEEE International Conference on Evolutionary Computation, pp. 188–192 (1995)

    Google Scholar 

  4. Satoh, H., Yamamura, M., Kobayashi, S.: Minimal generation gap model for GAs considering both exploration and exploitation. In: Proceedings of the IIZUKA 1996, pp. 494–497 (1996)

    Google Scholar 

  5. Mühlenbein, H.: The equation for response to selection and its use for prediction. J. Evol. Comput. 5(3), 303–346 (1997)

    Article  Google Scholar 

  6. Baluja, S.: Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, Technical Report, Carnegie Mellon University (1994)

    Google Scholar 

  7. Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evol. Comput. 3(4), 287–297 (1999)

    Article  Google Scholar 

  8. Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85, 461–464 (2000)

    Article  Google Scholar 

  9. Granger, G.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)

    Article  Google Scholar 

  10. Yang, X.S.: Test problems in optimization. In: Xin-She, Y. (ed.) Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010)

    Chapter  Google Scholar 

  11. Koabayashi, S.: The frontiers of real-coded genetic algorithms. J. Japanese Soc. Artif. Intell. 24(1), 128–143 (2009). (in Japanese)

    Google Scholar 

  12. Scrucca, L.: GA: a package for genetic algorithms in R. J. Stat. Softw. 53(4), 1–37 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hiroshi Sato .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sato, H. (2020). Evaluation of Search Performance of Evolutionary Computation by Transfer Entropy. In: Sato, H., Iwanaga, S., Ishii, A. (eds) Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. IES 2019. Proceedings in Adaptation, Learning and Optimization, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-37442-6_21

Download citation

Publish with us

Policies and ethics