We investigate the mutation-selection dynamics for an evolutionarycomputation model based on Turing Machines that we introduced in a previousarticle. The use of Turing Machines allows for very simple mechanisms of code growthand code activation/inactivation through point mutations. To any value of thepoint mutation probability corresponds a maximum amount of active code that canbe maintained by selection and the Turing machines that reach it are said to beat the error threshold. Simulations with our model show that the Turingmachines population evolve towards the error threshold. Mathematical descriptions of the model point out that this behaviour is duemore to the mutation-selection dynamics than to the intrinsic nature of theTuring machines. This indicates that this result is much more general than themodel considered here and could play a role also in biological evolution.
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