Skip to main content

Advertisement

Springer Nature Link
Log in

Accuracy improvement of genetic algorithm for obtaining floating-point solution

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

The aim of our study is implementation of genetic algorithm (GA) in FPGA hardware. We use GA for obtaining floating-point solutions accurately. For this purpose, we propose applying a gray-coded floating-point format to GA to improve accuracy of the solutions. In this paper, we show the result of simulations using a gray-coded floating-point format. We evaluate performance of the proposed GA by obtaining solutions of five-dimensional Sphere function and two-dimensional Rosenbrock function. In these experimentations, we focused on mutation probability which is one of the parameters of GA for improving its accuracy. In the results, there was a trade-off between convergence speed and speed of finding the optimal solution depending on the mutation probability. However, we showed that our theory can obtain the optimal solutions effectively compared with the normal floating-point format.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  2. Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, UK

    MATH  Google Scholar 

  3. Graham P, Nelson B (1995) A hardware genetic algorithm for the traveling salesman problem on SPLASH2. Proc Int Workshop Field Lect Notes Comput Sci 975:352–361

    Article  Google Scholar 

  4. Pakhira MK, De RK (2005) A hardware pipeline for function optimization using genetic algorithms. GECCO’05:25–29

  5. Chen ZQ, Wang RL (2010) An efficient real-coded genetic algorithm for real-parameter optimization. Sixth Int Conf Nat Comput:2276–2280

  6. Meenakshi, Gupta S (2014) Advanced level cyclic gray codes with application. Int J Electron Commun Comput Technol:619–622

  7. Belew RK, Vose MD (1997) Foundations of genetic algorithms 4. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

Download references

Acknowledgments

This work was supported by Tokyo Denki University Science Promotion Fund (Q12 J-03).

Author information

Authors and Affiliations

  1. Graduate School of Engineering, Tokyo Denki University, 5 Senju-Asahi-cho, Adachi-ku, Tokyo, 120-8551, Japan

    Kengo Nishijima, Akinori Kanasugi & Ki Ando

Authors
  1. Kengo Nishijima
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Akinori Kanasugi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ki Ando
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Akinori Kanasugi.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nishijima, K., Kanasugi, A. & Ando, K. Accuracy improvement of genetic algorithm for obtaining floating-point solution. Artif Life Robotics 19, 328–332 (2014). https://doi.org/10.1007/s10015-014-0174-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-014-0174-9

Keywords