Using Genetic Programming for Turing Machine Induction

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 metho- dologies for generating Turing machines. The genetic programming system implemented evolved general solutions for all ten problems.

• Type: Conference
• Year: 2008
• Where: EUROGP
• Authors: Amashini Naidoo, Nelishia Pillay
• Keywords: EUROGP 2008, Genetic Programming, Optimization, Turing Machine Transducer, Turing Machines