A finite automaton learning system using genetic programming
This report describes the Finite Automaton Learning System (FALS), an evolutionary system that is designed to find small digital circuits that duplicate the behavior 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.