Skip to main content

Genetic algorithms and relational landscapes

  • Modifications and Extensions of Evolutionary Algorithms Further Modifications and Extensionds
  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

Included in the following conference series:

Abstract

A DGA is a genetic algorithm with novel features: relational schemata. These structures allow a more natural expression of relations existing between loci. Indeed, schemata in standard genetic algorithms can only specify values for each locus. Relational schemata are based on the notion of duality: a schema can be represented by two strings. The intent of this paper is to show the superiority of DGAs over conventional genetic algorithms in two general areas: efficiency and reliability. Thus, we show with theoretical and experimental results, that our algorithm is faster and perform consistently. The application chosen for test DGAs is the optimization of an extension of Royal Road functions we call relational landscapes.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. P. Collard and J.P. Aurand. DGA: An efficient genetic algorithm. In A.G. Cohn, editor, ECAI'94: European Conference on Artificial Intelligence, pages 487–491. John Wiley & Sons, 1994.

    Google Scholar 

  2. P. Collard and C. Escazut. Genetic operators in a dual genetic algorithm. In ICTAI'95: Proceedings of the seventh IEEE International Conference on Tools with Artificial Intelligence, pages 12–19. IEEE Computer Society Press, 1995.

    Google Scholar 

  3. P. Collard and C. Escazut. Relational schemata: A way to improve the expressiveness of classifiers. In L. Eshelman, editor, ICGA '95: Genetic algorithms and their applications: Proceedings of the Sixth International Conference on Genetic Algorithms, pages 397–404, San Francisco, CA, 1995. Morgan Kaufmann.

    Google Scholar 

  4. P. Collard and C Escazut. Fitness Distance Correlation in a Dual Genetic Algorithm. In ECAI 96: 12th European Conference on Artificial Intelligence, 1996. To appear.

    Google Scholar 

  5. S. Forrest and M. Mitchell. Towards a stronger building-blocks hypothesis: Effects of relative building-blocks fitness on ga performance. In L. D. Whitley, editor, Foundations of Genetic Algorithms 2, pages 109–126. Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  6. D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley, 1989.

    Google Scholar 

  7. D. E. Goldberg, K. Deb, and J. Horn. Massive multimodality, deception and genetic algorithms. Technical Report 92005, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, Il 61801, 1992.

    Google Scholar 

  8. J. H. Holland. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press, 1975.

    Google Scholar 

  9. M. Mitchell, S. Forrest, and J. H. Holland. The royal road for genetic algorithms: Fitness landscape and GA performance. In F.J Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life, pages 245–254, Cambridge, MA, 1992. MIT Press/Bradford Books.

    Google Scholar 

  10. N. J. Radcliffe. Forma analysis and random respectful recombination. In R. K. Belew and L. B. Booker, editors, ICGA'91: Genetic algorithms and their applications: Proceedings of the Fourth International Conference on Genetic Algorithms, pages 222–229, San Mateo, CA, 1991. Morgan Kaufmann.

    Google Scholar 

  11. L. Shu and J. Schaeffer. VCS: Variable classifier systems. In J. D. Schaffer, editor, ICGA'89: Genetic algorithms and their applications: Proceedings of the Third International Conference on Genetic Algorithms, pages 334–339, San Mateo, CA, 1989. Morgan Kaufmann.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Collard, P., Escazut, C., Gaspar, A. (1996). Genetic algorithms and relational landscapes. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1011

Download citation

  • DOI: https://doi.org/10.1007/3-540-61723-X_1011

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics