Skip to main content

Parallel Genetic Algorithms, Parameters and Design

  • Conference paper
  • First Online:
Artificial Intelligence and Smart Environment (ICAISE 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 635))

  • 791 Accesses

Abstract

Genetic Algorithms are widely used in the quest of optimization of real-world complex problems. Parallel Genetic Algorithms may be considered as an evolution of the traditional GA. However, they are designed differently. Designing a Parallel Genetic Algorithm depends on many parameters other than the selection, the crossover, and the mutation parameters. In the literature, we can run into multiple and confusing Parallel Genetic Algorithms model names. These models are a sort of combination of specific parameters. Some of these parameters are the number of populations, the granularity of each population, the migration operation, the overlapping operation. This article lists these parameters used to design Parallel Genetic Algorithms so that the practitioner can design his own model, knowing the meaning and the use of each parameter.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
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

Similar content being viewed by others

References

  1. Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. Michigan Press, Ann Arbour (1975)

    Google Scholar 

  2. Vose, M.D.: The Simple Genetic Algorithm: Foundations and Theory. Complex Adaptive System, Complex ad. (1999)

    Google Scholar 

  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc., Boston (1989)

    Google Scholar 

  4. Nowostawski, M., Poli, R.: Parallel genetic algorithm taxonomy. In: 1999 Proceedings of Third International Conference on Knowledge-Based Intelligent Information Engineering Systems (Cat. No.99TH8410), pp. 88–92. IEEE (1999)

    Google Scholar 

  5. Cantú-Paz, E., Goldberg., D.E.: predicting speedups of idealized bounding cases of parallel genetic algorithms. In: Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 113–120 (1997)

    Google Scholar 

  6. Cantú-Paz, E., Goldberg, D.E.: Efficient parallel genetic algorithms: theory and practice. Comput. Methods Appl. Mech. Eng. 186, 221–238 (2000).

    Google Scholar 

  7. Cantú-Paz, E., Goldberg, D.E.: On the scalability of parallel genetic algorithms. Evol. Comput. 7, 429–449 (1999)

    Google Scholar 

  8. Wahib, M., Munawar, A., Munetomo, M., Akama, K.: Optimization of parallel genetic algorithms for nVidia GPUs. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 803–811. IEEE (2011)

    Google Scholar 

  9. Cantú-Paz, E., Goldberg, D.: Modeling idealized bounding cases of parallel genetic algorithms. In: 1997 Proceedings of Second Annual Conference on Genetic Programming, pp. 353–361 (1996)

    Google Scholar 

  10. Cantú-Paz, E.: A Survey of parallel genetic algorithms. Calc. Paralleles. 10, 141–171 (1998).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mustapha Ouiss .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ouiss, M., Ettaoufik, A., Marzak, A., Tragha, A. (2023). Parallel Genetic Algorithms, Parameters and Design. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-031-26254-8_95

Download citation

Publish with us

Policies and ethics