Causality of hierarchical variable length representations
Christian Igel · 2002
In this paper, the strong causality of program tree representations is considered. A quantitative, probabilistic causality measure is used in contrast to statistical fitness landscape analysis methods. Although it fails to rank different problems according to their difficulty, it is helpful for choosing the right coding for a given task. The investigation utilizes a metric on the search space called the tree edit distance. Different ways to define such a measure are discussed.