Level-Based Analysis of Genetic Algorithms and Other Search Processes

  • Dogan Corus
  • Duc-Cuong Dang
  • Anton V. Eremeev
  • Per Kristian Lehre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)


The fitness-level technique is a simple and old way to derive upper bounds for the expected runtime of simple elitist evolutionary algorithms (EAs). Recently, the technique has been adapted to deduce the runtime of algorithms with non-elitist populations and unary variation operators [2,8]. In this paper, we show that the restriction to unary variation operators can be removed. This gives rise to a much more general analytical tool which is applicable to a wide range of search processes. As introductory examples, we provide simple runtime analyses of many variants of the Genetic Algorithm on well-known benchmark functions, such as OneMax, LeadingOnes, and the sorting problem.


Genetic Algorithm Evolutionary Algorithm Evolutionary Computation Crossover Operator Runtime Analysis 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Dogan Corus
    • 1
  • Duc-Cuong Dang
    • 1
  • Anton V. Eremeev
    • 2
  • Per Kristian Lehre
    • 1
  1. 1.University of NottinghamUnited Kingdom
  2. 2.Omsk Branch of Sobolev Institute of MathematicsRussia

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