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Evolutionary Computation for Evolutionary Theory

  • C. C. Maley
Part of the Natural Computing Series book series (NCS)

Abstract

Evolutionary computation has developed primarily as an engineering tool for finding solutions to difficult problems. In the shadow of this success, a revolution has been building in evolutionary theory. Evolutionary models, often quite similar to genetic algorithms, are being used to extend our theoretical understanding of biology. Evolutionary models can represent details of the biology that makes analytical models mathematically intractable. Evolutionary models may thus be used as checks on the simplifications of analytical models, as well as formalisms for exploring the consequences of a particular representation of a biological system. In the best of cases, evolutionary models act as biological theories, accounting for previous experimental results and making predictions for future results. In this way, evolutionary models are serving to reunite theoretical biology with experimental biology.

“Mathematical and computational approaches to biological questions, a marginal activity a short time ago, are now recognized as providing some of the most powerful tools in learning about nature; such approaches guide empirical work and provide a framework for synthesis and analysis.”[1]

Keywords

Genetic Algorithm Mutation Rate Fossil Record Evolutionary Computation Configuration Model 
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

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • C. C. Maley
    • 1
  1. 1.Fred Hutchinson Cancer Research CenterSeattleUSA

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