  Fitness landscapes have historically been a powerful tool for analyzing the
search space explored by evolutionary algorithms. In particular, they
facilitate understanding how easily reachable an optimal solution is from a
given starting point. However, simple fitness landscapes are inappropriate for
analyzing the search space seen by selection schemes like lexicase selection in
which the outcome of selection depends heavily on the current contents of the
population (i.e. selection schemes with complex ecological dynamics). Here, we
propose borrowing a tool from ecology to solve this problem: community assembly
graphs. We demonstrate a simple proof-of-concept for this approach on an NK
Landscape where we have perfect information. We then demonstrate that this
approach can be successfully applied to a complex genetic programming problem.
While further research is necessary to understand how to best use this tool, we
believe it will be a valuable addition to our toolkit and facilitate analyses
that were previously impossible.

Neural and Evolutionary Computing