Advertisement

Heterogeneous Co-evolving Parasites

  • J. Holmes
  • T. W. Routen
  • C. A. Czarnecki
Conference paper

Abstract

This paper investigates a development of Hillis’ co-evolving parasites scheme and looks at how it might be used for job-shop scheduling problems. It suggests that a problem defined by orthogonal constraints may be solved using multiple types of co-evolving parasite.

Areas of specialisation permit the development of solutions specialised for certain constraints; overlaps of these areas create hybrids which combat multiple kinds of parasite and therefore provide solutions.

The main advantage of such a technique is that it permits the use of an evolutionary approach without requiring the specification of a fitness function, which is problematic for many real-world problems.

Keywords

Schedule Problem Parasite Gene Valid Region Sorting Network Valid Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    DAVIDOR, Y. (1991) A naturally occurring niche and species phenomenon: the model and first results, in R.K. Belew and L.B. Booker (eds.), Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufman.Google Scholar
  2. [2]
    DAVIS, L. (1985) Job shop scheduling with genetic algorithms. In J.J. Grefenstette (ed.), Proceedings of the First International Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates.Google Scholar
  3. [3]
    FANG, H.L., ROSS, P., and CORNE, D. (1993) A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems. In S. Forrest (ed.), Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann.Google Scholar
  4. [4]
    HILLIS, W. D. (1990) Co-evolving parasites improve simulated evolution as an optimisation procedure. In S. Forrest (ed.), Emergent Computation: Self-Organising, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks, 228–234, MIT Press.Google Scholar
  5. [5]
    HOLLAND, J. (1992) Adaptation in Natural and Artificial Systems, MIT Press.Google Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • J. Holmes
    • 1
  • T. W. Routen
    • 1
  • C. A. Czarnecki
    • 1
  1. 1.Department of Computer ScienceDe Montfort UniversityLeicesterUK

Personalised recommendations