Skip to main content
SpringerLink
Log in

New Generic Hybrids Based upon Genetic Algorithms

  • Conference paper
  • First Online:
Advances in Artificial Intelligence — IBERAMIA 2002 (IBERAMIA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2527))

Included in the following conference series:

Abstract

In this paper we propose some generic extensions to the general concept of a Genetic Algorithm. These biologically and sociologically inspired interrelated hybrids aim to make the algorithm more open for scalability on the one hand, and to retard premature convergence on the other hand without necessitating the development of new coding standards and operators for certain problems. Furthermore, the corresponding Genetic Algorithm is unrestrictedly included in all of the newly proposed hybrid variants under special parameter settings. The experimental part of the paper discusses the new algorithms for the Traveling Salesman Problem as a well documented instance of a multimodal combinatorial optimization problem achieving results which significantly outperform the results obtained with a conventional Genetic Algorithm using the same coding and operators.

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

Access this chapter

Log in via an institution
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 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

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.

References

  1. Affenzeller, M.: A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA). Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Lecture Notes of Computer Science 2084 (2001) 594–601

    Google Scholar 

  2. Affenzeller, M.: Transferring the Concept of Selective Pressure from Evolutionary Strategies to Genetic Algorithms. Proceedings of the 14th International Conference on Systems Science 2 (2001) 346–353

    Google Scholar 

  3. Affenzeller, M.: Segregative Genetic Algorithms (SEGA): A Hybrid Superstructure Upwards Compatible to Genetic Algorithms for Retarding Premature Convergence. Internatinal Journal of Computers, Systems and Signals (IJCSS), Vol. 2, Nr. 1 (2001) 18–32

    Google Scholar 

  4. Cobb, H.J., Grefenstette J.J.: Genetic Algorithms for Tracking Changing Environment. Proceedings of the Fifth International Conference on Genetic Algorithms (1993) 523–530

    Google Scholar 

  5. Fogel, D.B.: An Introduction to Simulated Evolutionary Optimization. IEEE Trans. on Neural Networks 5(1) (1994) 3–14

    Article  Google Scholar 

  6. Goldberg, D. E.: Genetic Alogorithms in Search, Optimization and Machine Learning. Addison Wesley Longman (1989)

    Google Scholar 

  7. Holland, J. H.: Adaption in Natural and Artificial Systems. 1st MIT Press ed. (1992)

    Google Scholar 

  8. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220 (1983) 671–680

    Article  MathSciNet  Google Scholar 

  9. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer-Verlag, Berlin Heidelberg New York (1996)

    MATH  Google Scholar 

  10. Rechenberg, I.: Evolutionsstrategie. Friedrich Frommann Verlag (1973)

    Google Scholar 

  11. Reinelt, G.: TSPLIB-A Traveling Salesman Problem Library. ORSA Journal on Computing 3 (1991) 376–384

    MATH  Google Scholar 

  12. Schöneburg, E., Heinzmann, F., Feddersen, S.: Genetische Algorithmen und Evolutionsstrategien. Addison-Wesley (1994)

    Google Scholar 

  13. Smith, R.E., Forrest, S., Perelson, A.S.: Population Diversity in an Immune System Model: Implications for Genetic Search. Foundations of Genetic Algorithms 2 (1993) 153–166

    Google Scholar 

  14. Srinivas, M., Patnaik, L.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics 24(4) (1994) 656–667

    Article  Google Scholar 

  15. Wendt, O.: Tourenplanung durch Einsatz naturanaloger Verfahren. Deutscher Universitätsverlag (1995)

    Google Scholar 

  16. Whitley, D.: A Genetic Algorithm Tutorial. Statistics and Computing 4 (1994) 65–85

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Systems Science Systems Theory and Information Technology, Johannes Kepler University, Altenbergerstrasse 69, A-4040, Linz, Austria

    Michael Affenzeller

Authors
  1. Michael Affenzeller
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Telefónica Investigación y Desarrollo, Emilio Vargas 6, 28043, Madrid, Spain

    Francisco J. Garijo

  2. Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, ETS Ingeniería Informática, 41012, Seville, Spain

    José C. Riquelme  & Miguel Toro  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Affenzeller, M. (2002). New Generic Hybrids Based upon Genetic Algorithms. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_34

Download citation

  • DOI: https://doi.org/10.1007/3-540-36131-6_34

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation