On self-adaptive features in real-parameter evolutionary algorithms

  • Authors:
  • H. -G. Beyer;K. Deb

  • Affiliations:
  • Dept. of Comput. Sci., Dortmund Univ.;-

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2001

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Abstract

Due to the flexibility in adapting to different fitness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications in real-valued search spaces. Specifically, population mean and variance of a number of SA-EA operators such as various real-parameter crossover operators and self-adaptive evolution strategies are calculated for this purpose. Simulation results are shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive genetic algorithms and evolution strategies have shown similar performance in the past and also suggest appropriate strategy parameter values, which must be chosen while applying and comparing different SA-EAs