  The notion of building blocks can be related to the structure of the
offspring probability distribution: loci of which variability is strongly
correlated constitute a building block. We call this correlated exploration.
With this background we analyze the structure of the offspring probability
distribution, or exploration distribution, for a GA with mutation only, a
crossover GA, and an Estimation-Of-Distribution Algorithm (EDA). The results
allow a precise characterization of the structure of the crossover exploration
distribution. Essentially, the crossover operator destroys mutual information
between loci by transforming it into entropy; it does the inverse of correlated
exploration. In contrast, the objective of EDAs is to model the mutual
information between loci in the fitness distribution and thereby they induce
correlated exploration.
