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NeuroCOLT
Technical Report NC-TR-95-009
A
Finite Automaton Learning System using Genetic Programming
Herman
Ehrenburg
CWI
Jeroen
van Maanen
CWI
Abstract
This report describes the Finite Automaton Learning System (FALS),
an evolutionary system that is designed to find small digital circuits
that duplicate the behaviour of a given finite automaton. FALS is
developed with the aim to get a better insight in learning systems.
It is also targeted to become a general purpose automatic programming
system. The system is based on the genetic programming approach to
evolve programs for tasks instead of explicitly programming them.
A representation of digital circuits suitable for genetic programming
is given as well as an extended crossover operator that alleviates
the need to specify an upper bound for the number of states in advance.
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Postscript
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