Abstract
Turing machines are playing an increasingly significant role in Computer Science domains such as bioinformatics. Instead of directly formulating a solution to a problem, a Turing machine which produces a solution algorithm is generated. The original problem is reduced to that of inducing an acceptor for a recursively enumerable language or a Turing machine transducer. This paper reports on a genetic programming system implemented to evolve Turing machine acceptors and transducers. Each element of the population is represented as a directed graph and graph crossover, mutation and reproduction are used to evolve each generation. The paper also presents a set of five acceptor and five transducer benchmark problems which can be used to test and compare different methodologies for generating Turing machines. The genetic programming system implemented evolved general solutions for all ten problems.
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Naidoo, A., Pillay, N. (2008). Using Genetic Programming for Turing Machine Induction. In: O’Neill, M., et al. Genetic Programming. EuroGP 2008. Lecture Notes in Computer Science, vol 4971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78671-9_30
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DOI: https://doi.org/10.1007/978-3-540-78671-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78670-2
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