Journal of Heuristics

, Volume 5, Issue 2, pp 145–158 | Cite as

A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization

  • Patrice Calégari
  • Giovanni Coray
  • Alain Hertz
  • Daniel Kobler
  • Pierre Kuonen
Article

Abstract

This paper shows how evolutionary algorithms can be described in a concise, yet comprehensive and accurate way. A classification scheme is introduced and presented in a tabular form called TEA (Table of Evolutionary Algorithms). It distinguishes between different classes of evolutionary algorithms (e.g., genetic algorithms, ant systems) by enumerating the fundamental ingredients of each of these algorithms. At the end, possible uses of the TEA are illustrated on classical evolutionary algorithms.

evolutionary algorithms genetic algorithms taxonomy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bäck, Th. and H.-P. Schwefel. (1993). “An Overview of Evolutionary Algorithms for Parameter Optimization,” Evolutionary Computation 1, 1–23.Google Scholar
  2. Colorni, A., M. Dorigo, and V. Maniezzo. (1991). “Distributed Optimization by Ant Colonies.” In MIT Press (ed.), First European Conference on Artificial Life, Bradford Books, pp. 134–142.Google Scholar
  3. Colorni, A., M. Dorigo, and V. Maniezzo. (1992). “An Investigation of Some Properties of an Ant Algorithm.” In R. Männer and B. Manderick (eds.), Second European Conference on Parallel Problem Solving from Nature. Brussels: Elsevier Publishing, pp. 509–520.Google Scholar
  4. Davis, L. (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.Google Scholar
  5. Glover, F. (1977). “Heuristics for Integer Programming Using Surrogate Constraints,” Decision Sciences 8, 156–166.Google Scholar
  6. Glover, F. (1994). “Genetic Algorithms and Scatter Search: Unsuspected Potentials,” Statistics and Computing 4, 131–140.Google Scholar
  7. Glover and M. Laguna. (1997). Tabu Search. Norwell, MA: Kluwer Academic Publishers.Google Scholar
  8. Goldberg, D. (1989). Genetics Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley: Publishing Company.Google Scholar
  9. Heitkötter, J. and D. Beasley. (1997). The Hitch-Hiker's Guide to Evolutionary Computation (FAQ for comp.ai.genetic). URL:ftp://ftp.krl.caltech.edu/pub/EC/Welcome.html.Google Scholar
  10. Holland, J. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.Google Scholar
  11. Kuntz, P. and D. Snyers. (1994). “Emergent Colonization and Graph Partitioning,” Third International Conference of Adaptative Behavior. MIT Press, pp. 494–500.Google Scholar
  12. Rochat, Y. and E. Taillard. (1995). “Probabilistic Diversification and Intensification in Local Search for Vehicle Routing,” Journal of Heuristics 1, 147–167.Google Scholar
  13. Spears, W.M. and K. DeJong. (1991). “An Analysis of Multi-Point Crossover.” In G.J.E. Rawlins (ed.), Foundations of Genetic Algorithms. Morgan Kaufmann, pp. 301–315.Google Scholar
  14. Syswerda, G. (1989). “Uniform Crossover in Genetic Algorithms.” In J.D. Schaffer (ed.), Third International Conference on Genetic Algorithms. Morgan Kaufmann, pp. 2–9.Google Scholar
  15. Zufferey, N. and A. Hertz. (1997). “Coloration de graphes `a l'aide de fourmis.” Technical Report, EPFL, Lausanne, Switzerland.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Patrice Calégari
    • 1
  • Giovanni Coray
    • 1
  • Alain Hertz
    • 1
  • Daniel Kobler
    • 1
  • Pierre Kuonen
    • 1
  1. 1.Department of Mathematics and Computer ScienceSwiss Federal Institute of TechnologyLausanneSwitzerland

Personalised recommendations