Optimization by Diploid Search Strategies

  • W. Banzhaf
Conference paper
Part of the Springer Series in Synergetics book series (SSSYN, volume 42)


We present some results on optimization of cost functions using a population of parallel processors. Based on a simulated evolutionary search strategy diploid recombination is introduced as a means for maintaining variability in computational problems with large numbers of local extrema. The new strategy is compared to some traditional algorithms simulating evolution.


Cost Function Continue Fraction Gene String Gradient Search Phenotype Space 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M.R. Garey, D.S. Johnson: Computers and Intractability ( W.H.Freeman, New York 1979 )MATHGoogle Scholar
  2. 2.
    E. Lawler: Combinatorial Optimization, Networks and Matroids ( Rinehart and Winston, New York 1976 )MATHGoogle Scholar
  3. 3.
    H.J. Bremermann, M. Rogson, S. Salaff: In Biophysics and Cybernetic Systems, ed. by M. Maxfield, A. Callahan, L.J. Fogel ( Spartan Books, Washington 1965 )Google Scholar
  4. 4.
    D. Cohen: In Biogenesis, Evolution, Homeostasis, ed. by A. Locker ( Springer Verlag, Berlin 1973 )Google Scholar
  5. 5.
    I. Rechenberg: Evolutionsstrategie, ( Holtzmann Froboog, Stuttgart 1973 )Google Scholar
  6. 6.
    J.H. Holland: Adaption in natural and artificial Systems, ( University of Michigan Press, Ann Arbor 1975 )Google Scholar
  7. 7.
    W.Ebeling, R.Feistel: Ann. Phys. (Leipzig), 34, 81 (1977)ADSGoogle Scholar
  8. 8.
    H.P. Schwefel: Numerical Optimization of Computer Models, ( Wiley, New York 1981 )MATHGoogle Scholar
  9. 9.
    M. Eigen: Ber. Bunsenges. Phys. Chem. 89, 658 (1985)Google Scholar
  10. 10.
    P. Schuster: Chem. Scr. 26B, 27 (1986)Google Scholar
  11. 11.
    S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi: Science 220, 671 (1983)MathSciNetADSMATHCrossRefGoogle Scholar
  12. 12.
    M. Conrad: Adaptability ( Plenum Press, New York 1983 )CrossRefGoogle Scholar
  13. 13.
    see: Evolution, Games and Learning- Models for Adaption in Machines and Nature, ed. by D. Farmer, A. Lapedes, N. Packard, B. Wendroff, Physica D 22, 1986Google Scholar
  14. 14.
    H. Haken: In Computational Systems - Natural and Artificial, ed. by H. Haken (Springer, Berlin, New York 1987 )CrossRefGoogle Scholar
  15. 15.
    D.H. Ackley: A Connectionist Machine for Genetic Hillclimbing (Kluwer Academic Publishers 1987)Google Scholar
  16. 16.
    W.Banzhaf: BioSystems, 1988, in pressGoogle Scholar
  17. 17.
    M.R. Schroeder: Number Theory in Science and Communication, 2nd. ed. (Springer, Berlin, New York 1986 )Google Scholar
  18. 18.
    0. Perron: Die Lehre von den Kettenbrüchen, 3rd. ed. ( Teubner, Stuttgart 1977 )Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • W. Banzhaf
    • 1
  1. 1.Institut für Theoretische PhysikSynergetik der Universität StuttgartStuttgart 80Fed. Rep. of Germany

Personalised recommendations