Effect of Population Size in Extended Parameter-Free Genetic Algorithm

  • Susumu Adachi
Conference paper
Part of the Proceedings in Information and Communications Technology book series (PICT, volume 2)


We propose an extended parameter-free genetic algorithm. The first step of this study is that each individual includes additional gene whose phenotype indicates a mutation rate. The second step is an extension of the selection rule of the parameter-free genetic algorithm, in which each individual has a characteristic neighborhood radius and the individuals generated near the parents are not selected to avoid trapping a local minimum. The characteristic neighborhood radius of an individual is given by the distance between before mutation and after mutation. As a result of the experiment for function minimization problems, effect of the population size appears and the success rate is improved.


Genetic Algorithm Population Size Mutation Rate Selection Rule Evolutionary Computation 
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 Tokyo 2010

Authors and Affiliations

  • Susumu Adachi
    • 1
  1. 1.National Institute of Information and Communications TechnologyNano ICT GroupJapan

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