Scale-invariance of ruggedness measures in fractal fitness landscapes
This work addresses the characterization of fitness landscapes in chaotic systems, which is incremental as it applies existing measures to new data without introducing novel methods.
The paper tackled the problem of analyzing ruggedness and fractality in fitness landscapes generated by chaotic maps for targeting problems, showing that ruggedness measures like correlation length and information content are scale-invariant and self-similar.
The paper deals with using chaos to direct trajectories to targets and analyzes ruggedness and fractality of the resulting fitness landscapes. The targeting problem is formulated as a dynamic fitness landscape and four different chaotic maps generating such a landscape are studied. By using a computational approach, we analyze properties of the landscapes and quantify their fractal and rugged characteristics. In particular, it is shown that ruggedness measures such as correlation length and information content are scale-invariant and self-similar.