Abstract
This paper explores the possibility of using the evolution of a population of finite state machines (FSMs) as a measure of the ‘randomness’ of a given binary sequence. An FSM with binary input and output alphabet can be seen as a predictor of a binary sequence. For any finite binary sequence, there exists an FSM able to perfectly predict the string but such a predictor, in general, has a large number of states. In this paper, we address the problem of finding the best predictor for a given sequence. This is an optimization problem over the space of all possible FSMs with a fixed number of states evaluated on the sequence considered. For this optimization an evolutionary algorithm is used: the better the FSMs found are, the less ‘randomés the given sequence will be.
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Cerruti, U., Giacobini, M., Liardet, P. (2002). Prediction of Binary Sequences by Evolving Finite State Machines. In: Collet, P., Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2001. Lecture Notes in Computer Science, vol 2310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46033-0_4
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DOI: https://doi.org/10.1007/3-540-46033-0_4
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