  Among the wide variety of evolutionary computing models, Finite State
Machines (FSMs) have several attractions for fundamental research. They are
easy to understand in concept and can be visualised clearly in simple cases.
They have a ready fitness criterion through their relationship with Regular
Languages. They have also been shown to be tractably evolvable, even up to
exhibiting evidence of open-ended evolution in specific scenarios. In addition
to theoretical attraction, they also have industrial applications, as a
paradigm of both automated and user-initiated control. Improving the
understanding of the factors affecting FSM evolution has relevance to both
computer science and practical optimisation of control. We investigate an
evolutionary scenario of FSMs adapting to recognise one of a family of Regular
Languages by categorising positive and negative samples, while also being under
a counteracting selection pressure that favours fewer states. The results
appear to indicate that longer strings provided as samples reduce the speed of
fitness gain, when fitness is measured against a fixed number of sample
strings. We draw the inference that additional information from longer strings
is not sufficient to compensate for sparser coverage of the combinatorial space
of positive and negative sample strings.
