Development of problem-specific evolutionary algorithms

  • Alexander Leonhardi
  • Wolfgang Reissenberger
  • Tim Schmelmer
  • Karsten Weicker
  • Nicole Weicker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


It is a broadly accepted fact that evolutionary algorithms (EA) have to be developed problem-specifically. Usually this is based on experience and experiments. Though, most EA environments are not suited for such an approach. Therefore, this paper proposes a few basic concepts which should be supplied by modern EA simulators in order to serve as a toolkit for the development of such algorithms.


Genetic Algorithm Evolutionary Algorithm Fitness Function Threshold Algorithm Travel Salesperson Problem 
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.


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  1. [AAD97]
    K. Alicke, D. Arnold, and V. Dörrsam. (R)evolution in layout-planning — a new hybrid genetic algorithm to generate optimal aisles in a utility layout. In H.-J. Zimmermann, editor, Eufit 97 — 5th European Congress on Intelligent Techniques and Soft Computing, pages 788–793, Aachen, 1997. Verlag Mainz, Wissenschaftsverlag, Aachen.Google Scholar
  2. [AJK+95]
    F. Amos, K. Jung, B. Kawetzki, W. Kuhn, O. Pertler, R. Reißing, and M. Schaal. Endbericht der Projektgruppe Genetische Algorithmen. Technical Report FK95/1, University of Stuttgart, Institute of Computer Science, Dept. Formal Concepts, 1995. german.Google Scholar
  3. [Ban94]
    W. Banzhaf. Genotype-phenotype-mapping and neutral variation — a case study in genetic programming. In Davidor et al. [DSM94].Google Scholar
  4. [BB91]
    R.K. Belew and L.B. Booker, editors. Proceedings of the Fourth International Conference on Genetic Algorithms — ICGA V, San Mateo, California, 1991. Morgan Kaufmann.Google Scholar
  5. [BFM97]
    T. Bäck, D.B. Fogel, and Z. Michalewiez, editors. Handbook of Evolutionary Computation. IOP Puplishing Ltd and Oxford University Press, 1997.Google Scholar
  6. [BHS91]
    T. Bäck, F. Hoffmeister, and H.-P. Schwefel. A survey of evolution strategies. In Belew and Booker [BB91].Google Scholar
  7. [BMK96]
    C. Bierwirth, D. Mattfeld, and H. Kopfer. On permutation representations for scheduling problems. In Voigt et al. [VERS96].Google Scholar
  8. [DKF93]
    Laura Dekker, Jason Kingdon, and J. R. Filho. GAME Version 2.01, User's Manual. University College London, 1993.Google Scholar
  9. [DPT96]
    D. Duvivier, Ph. Preux, and E.-G. Talbi. Climbing up NP-hard hills. In Voigt et al. [VERS96].Google Scholar
  10. [DSM94]
    Y. Davidor, H.-P. Schwefel, and R. Maenner, editors. Parallel Problem Solving from Nature — PPSN III, volume 866 of Lecture Notes in Computer Science, Berlin, 1994. Springer-Verlag.Google Scholar
  11. [Fog92]
    D.B. Fogel. An analysis of evolutionary programming. In D.B. Fogel and J. W. Atmar, editors. Proceedings of the First annual Conference on Evolutionary Programming, La Jolla, 1992. Evolutionary Programming Society.Google Scholar
  12. [FRC93]
    H.-L. Fang, P. Ross, and D. Corne. A promising genetic algorithm approach to job-shop scheduling, rescheduling and open-shop scheduling problems. In Proceedings of the Fifth Int. Conf. on Genetic Algorithms, pages 375–382. Morgan Kaufmann Publishers, 1993.Google Scholar
  13. [GLS97]
    M. Großmann, A. Leonhardi, and T. Schmidt. Abschlußbericht der Projektgruppe Evolutionäre Algorithmen. Technical Report 2, University of Stuttgart, Institute of Computer Science, 1997. german.Google Scholar
  14. [Gol89]
    D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, 1989.zbMATHGoogle Scholar
  15. [Gre90]
    J.J. Grefenstette. A User's Guide to GENESIS, Version 5.0, 1990.Google Scholar
  16. [Her96]
    M. Herdy. Evolution strategies with subjective selection. In Voigt et al. [VERS96].Google Scholar
  17. [Hol75]
    J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.Google Scholar
  18. [IdGS94]
    H. Iba, H. de Garis, and T. Sato. Genetic programming with local hill-climbing. In Davidor et al. [DSM94].Google Scholar
  19. [JW95]
    K. Jung and N. Weicker. Funktionale Spezifikation des Software-Tools EAGLE. Technical Report FK95/2, University of Stuttgart, Institute of Computer Science, Dept. Formal Concepts, 1995. german.Google Scholar
  20. [Kaw96]
    B. Kawetzki. Topologieoptimierung diskreter Tragwerke mittels Evolutionsstrategien am Beispiel ebener Fachwerke. Master's thesis, University of Stuttgart, 1996. german.Google Scholar
  21. [Koz92]
    J.R. Koza. Genetic Programming. MIT Press, 1992.Google Scholar
  22. [Leo97]
    A. Leonhardi. Eine Beschreibungssprache für Evolutionäre Algorithmen. Master's thesis, University of Stuttgart, Institute of Computer Science, 1997. german.Google Scholar
  23. [LK73]
    S. Lin and B. Kernighan. An efficient heuristic procedure for the traveling salesman problem. Operations Res., 21:498–516, 1973.zbMATHMathSciNetCrossRefGoogle Scholar
  24. [MHI96]
    M. McIlhagga, P. Husbands, and R. Ives. A comparison of search techniques on a wing-box optimisation problem. In Voigt et al. [VERS96].Google Scholar
  25. [SR94]
    P.D. Surry and N.J. Radcliffe. RPL2: A language and parallel framework for evolutionary computing. In Davidor et al. [DSM94].Google Scholar
  26. [SS94]
    M. Sebag and M. Schoenauer. Controlling crossover through inductive learning. In Davidor et al. [DSM94].Google Scholar
  27. [TGF96]
    S. Tsutsui, A. Ghosh, and Y. Fujimoto. A robust solution searching scheme in genetic search. In Voigt et al. [VERS96].Google Scholar
  28. [VBT91]
    H.-M. Voigt, J. Born, and J. Treptow. The Evolution Machine, Manual, Version 2.1. Institute for Informatics and Computing Techniques, Berlin, 1991.Google Scholar
  29. [VERS96]
    H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors. Parallel Problem Solving from Nature — PPSN IV, volume 1141 of Lecture Notes in Computer Science, Berlin, 1996. Springer-Verlag.Google Scholar
  30. [VM97]
    D. Vigo and V. Maniezzo. A genetic/tabu thresholding hybrid algorithm for the process allocation problem. Journal of Heuristics, 3(2):91–110, 1997.zbMATHCrossRefGoogle Scholar
  31. [WGM94]
    D. Whitley, V.S. Gordon, and K. Mathias. Lamarckian evolution, the baldwin effect and function optimization. In Davidor et al. [DSM94].Google Scholar
  32. [WM97]
    D.H. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions On Evolutionary Computation, 1(1):67–82, April 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Alexander Leonhardi
    • 1
  • Wolfgang Reissenberger
    • 1
  • Tim Schmelmer
    • 2
  • Karsten Weicker
    • 3
  • Nicole Weicker
    • 1
  1. 1.Fakultät InformatikUniversität StuttgartGermany
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of KansasUSA
  3. 3.Wilhelm-Schickard-Institut für InformatikUniversität TübingenGermany

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