  The problem of learning pairwise disjoint deterministic finite automata (DFA)
from positive examples has been recently addressed. In this paper, we address
the problem of identifying a set of DFAs from labeled strings and come up with
two methods. The first is based on state merging and a heuristic related to the
size of each state merging iteration. State merging operations involving a
large number of states are extracted, to provide sub-DFAs. The second method is
based on a multi-objective evolutionary algorithm whose fitness function takes
into account the accuracy of the DFA w.r.t. the learning sample, as well as the
desired number of DFAs. We evaluate our methods on a dataset originated from
industry.
