I was poking around looking for some introductory material on the building of evolutionary programmming applications (no really, see below) and I randomly came across this paper. I noticed it because it seems to combine two of my current intellectual itches, genetic programmming and automata networks.

I can’t really understand the entire abstract so I doubt if I’ll make it through the actual paper, but as near as I can figure, dude is using networked evolutionary programming engines in order to predict what will be next in a pattern. This network of evolutionary predictor automata includes “switchboard” automata whose job it is to predict wich predictor will be the best one. I think. Wow. The best part is they tested the whole thing by seeing how well it could predict what was coming next in Bach?s Fugue IX. Ballsy. And I love the title.

On Synchronized Evolution of the Network of Automata

Abstract?One of the tasks in machine learning is to build a device
that predicts each next input symbol of a sequence as it takes
one input symbol from the sequence. We studied new approaches
to this task. We suggest that deterministic finite automata (DFA)
are good building blocks for this device together with genetic algorithms
(GAs), which let these automata ?evolve? to predict each
next input symbol of the sequence. Moreover, we studied how to
combine these highly fit automata so that a network of them would
compensate for each others? weaknesses and predict better than
any single automaton.We studied the simplest approaches to combine
automata: building trees of automata with special-purpose automata,
which may be called switchboards. These switchboard automata
are located on the internal nodes of the tree, take an input
symbol from the input sequence just as do other automata, and
predict which subtree will make a correct prediction on each next
input symbol. GAs again play a crucial role in searching for switchboard
automata. We studied various ways of growing trees of automata
and tested them on sample input sequences, mainly note
pitches, note duration, and up/down notes of Bach?s Fugue IX. The
test results show that DFAs together with GAs seem to be very effective
for this type of pattern learning task.