Results on a fractal measure for evolutionary optimization

Evolutionary optimizers employ independent Gaussian random variables as a central component for their processing, which often renders them immune to analysis. This paper investigates the applicability of the Hurst dimension, a fractal dimension, as a characterization of processing in an evolutionary optimizer. Results show that this fractal measure does highlight some interesting processing commonalities between standard and self-adaptive evolutionary optimization. A potentially worthwhile modification to evolutionary optimization is suggested based on the results.