An Analysis of the Intensification and Diversification Behavior of Different Operators for Genetic Algorithms

  • Andreas Scheibenpflug
  • Stefan Wagner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)


Intensification and diversification are two driving forces in genetic algorithms and are frequently the subject of research. While it seemed for decades that a genetic operator can be classified as either the one or the other, it has been shown in the last few years that this assumption is an oversimplified view and most operators exhibit both, diversification and intensification, to some degree. Most papers in this field focus on a certain operator or algorithm configuration as theoretical and generalizable foundations are hard to obtain. In this paper we therefore use a wide range of different configurations and behavior measurements to study the intensification and diversification behavior of genetic algorithms and their operators.


Genetic Algorithm Problem Instance Mutation Operator Travel Salesman Problem Crossover Operator 
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 2013

Authors and Affiliations

  • Andreas Scheibenpflug
    • 1
  • Stefan Wagner
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory (HEAL), School of Informatics, Communications and MediaUniversity of Applied Sciences Upper AustriaHagenbergAustria

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