On self-adaptive features in real-parameter evolutionary algorithms

Hans-Georg Beyer

doi:10.1109/4235.930314

On self-adaptive features in real-parameter evolutionary algorithms

2001-06-01

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AI

DOI：10.1109/4235.930314

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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.

Keywords:

blind crossover operator

evolution strategies

fuzzy recombination operator

genetic algorithms

population mean

population variance

self-adaptation

simulated binary crossover

test fitness landscapes

On self-adaptive features in real-parameter evolutionary algorithms

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