Parallel Varying Mutation in Deterministic and Self-adaptive GAs

  • Hernán E. Aguirre
  • Kiyoshi Tanaka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


In this work we study varying mutations applied either serial or parallel to crossover and discuss its effect on the performance of deterministic and self-adaptive varying mutation GAs. After comparative experiments, we found that varying mutation parallel to crossover can be a more effective framework in both deterministic and self-adaptive GAs to achieve faster convergence velocity and higher convergence reliability. Best performance is achieved by a parallel varying mutation self-adaptive GA.


Genetic Algorithm Mutation Rate Background Mutation Proportional Selection Convergence Velocity 
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 2002

Authors and Affiliations

  • Hernán E. Aguirre
    • 1
  • Kiyoshi Tanaka
    • 1
  1. 1.Faculty of EngineeringShinshu UniversityNaganoJAPAN

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