This paper addresses the problem of how and when learning is an aid to
evolutionary search in hierarchical modular tasks. It brings together two
major areas of research in evolutionary computation, the performance of
evolutionary algorithms on hierarchical modular tasks, and the role of
learning in evolutionary search, known as the Baldwin effect.  A new task
called the jester's cap is proposed, formed by adding learning to Watson,
Hornby and Pollack's Hierarchical-If-and-only-If, function, using the simple
guessing framework of Hinton and Nowlan's Baldwin effect simulations.
Whereas Hinton and Nowlan used a task with a single fitness peak, ideally
suited to learning, the jester's cap is a hierarchical task that has two
major fitness peaks and multiple sub-peaks.  We conducted a series of
simulations to explore the effect of different amounts of learning on the
jester's cap. The simulations demonstrate that learning aids evolution only
in search spaces in which the simplest level of modules are difficult to
find.  The learning mechanism explores local regions of the search space,
while crossover explores neighborhoods in higher-order modular spaces.