  There is a broad class of networks which connect inputs to outputs. We
provide a strong theoretical foundation for crossover across this class and
connect it to informativeness, a measure of the connectedness of inputs to
outputs. We define Input/Output Directed Graphs (or IOD Graphs) as graphs with
nodes $N$ and directed edges $E$, where $N$ contains (a) a set of "input nodes"
$I \subset N$, where each $i \in I$ has no incoming edges and any number of
outgoing edges, and (b) a set of "output nodes" $O \subset N$, where each $o
\in O$ has no outgoing edges and any number of incoming edges, and $I\cap O =
\emptyset$. We define informativeness, which involves the connections via
directed paths from the input nodes to the output nodes: A partially
informative IOD Graph has at least one path from an input to an output, a very
informative IOD Graph has a path from every input to some output, and a fully
informative IOD Graph has a path from every input to every output.
  A perceptron is an example of an IOD Graph. If it has non-zero weights and
any number of layers, it is fully informative. As links are removed (assigned
zero weight), the perceptron might become very, partially, or not informative.
  We define a crossover operation on IOD Graphs in which we find subgraphs with
matching sets of forward and backward directed links to "swap." With this
operation, IOD Graphs can be subject to evolutionary computation methods. We
show that fully informative parents may yield a non-informative child. We also
show that under conditions of contiguousness and the no dangling nodes
condition, crossover compatible, partially informative parents yield partially
informative children, and very informative input parents with partially
informative output parents yield very informative children. However, even under
these conditions, full informativeness may not be retained.
