Stopping Criteria for Single-Objective Optimization

@inproceedings{Zielinski2005StoppingCF,
  title={Stopping Criteria for Single-Objective Optimization},
  author={Karin Zielinski and Dagmar Peters and Rainer Laur},
  year={2005}
}
In most literature dealing with evolutionary algorithms the stopping criterion consists of reaching a certain number of objective function evaluations (or a number of generations, respectively). A disadvantage is that the number of function evaluations that is necessary for convergence is unknown a priori, so trialand-error methods have to be applied for finding a suitable number. By using other stopping criteria that include knowledge about the state of the optimization run this process can be… CONTINUE READING

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