On the Analysis of Self-Adaptive Evolutionary Algorithms
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Date
2001-10-16
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Universität Dortmund
Abstract
Due to the exibility 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. 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. In every case, simulation results are shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive GAs and ESs 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.
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Keywords
blend crossover operator, evolution strategies, fuzzy recombination operator, genetic algorithms, population mean, population variance, self-adaptation, simulated binary crossover