On self-adaptive features in real-parameter evolutionary algorithms

Beyer, H.-G. ; Deb, K. (2001) On self-adaptive features in real-parameter evolutionary algorithms IEEE Transactions on Evolutionary Computation, 5 (3). pp. 250-270. ISSN 1089-778X

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/4235.930314


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

Item Type:Article
Source:Copyright of this article belongs to Institute of Electrical and Electronic Engineers.
ID Code:9418
Deposited On:02 Nov 2010 12:15
Last Modified:31 May 2011 07:13

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