Genetic Algorithms and Protein Folding

  • Steffen Schulze-Kremer
Part of the Methods in Molecular Biology™ book series (MIMB, volume 143)


Genetic algorithms are, like neural networks, an example par excellence of an information-processing paradigm that was originally developed and exhibited by nature and later discovered by humans, who subsequently transformed the general principle into computational algorithms to be put to work in computers. Nature uses the principle of genetic heritage and evolution in an impressive way. Application of the simple concept of performance based reproduction of individuals (“survival of the fittest”) led to the rise of well-adapted organisms that can endure in a potentially adverse environment. Mutually beneficial interdependencies, cooperation, and even apparently altruistic behavior can emerge solely by evolution. The investigation of those phenomena is part of research in artificial life but is not dealt with here.


Genetic Algorithm Fitness Function Torsion Angle Genetic Operator Native Conformation 
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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Copyright information

© Humana Press Inc. 2000

Authors and Affiliations

  • Steffen Schulze-Kremer
    • 1
  1. 1.Max-Planck Institute for Molecular GeneticsBerlinGermany

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