Dominant Takeover Regimes for Genetic Algorithms

  • David Noever
  • Subbiah Baskaran
Conference paper


The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws [1–2]. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler’s device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solutions reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.


Genetic Algorithm Convergence Time Partial Summation Approximate Limit Genetic Algorithm Processing 
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. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan: University of Michigan Press 1975.Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Reading, Massachusetts: Addison Wesley 1989.Google Scholar
  3. 3.
    Cook, S.A.: Comm. of ACM 26, 401 (1983).CrossRefGoogle Scholar
  4. 4.
    Ankenbrandt, C.: (1991) “Time Complexity and Convergence of Genetic Algorithms”.Rawlins, G.J.E. (ed.): Foundations of Genetic Algorithms. San Mateo, California: Morgan Kaufmann Publishers 1991.Google Scholar
  5. 5.
    Noever D. and Baskaran, S.: “Steady State vs. Generational Genetic Algorithms: A Comparison of Time Complexity and Convergence Properties” Santa Fe Institute preprint series, 92-07-032 (submitted to Machine Learning) (1992).Google Scholar
  6. 6.
    Goldberg, D.E. and Deb, K.: “A Comparative Analysis of Selection Schemes Used in Genetic Algorithms,” Rawlins, G.J.E. (ed.): Foundations of Genetic Algorithms. San Mateo, California: Morgan Kaufmann Publishers 1991.Google Scholar
  7. 7.
    Broucke, R.A.: Comm. of the ACM 14, 34 (1971).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • David Noever
    • 1
  • Subbiah Baskaran
    • 1
    • 2
  1. 1.Biophysics Branch, ES76 George C. Marshall Space Flight CenterNational Aeronautics and Space AdministrationHuntsvilleUSA
  2. 2.Institut fuer Molekulare BiotechnologieJenaGermany

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