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AbstractThe development of general edit metric decoders is a challenging problem, especially with the inclusion of additional biological restrictions that can occur when using error correcting codes in biological applications. Side effect machines (SEMs), an extension of finite state machines, can provide efficient decoding algorithms for such edit metric codes.Several codes of varying lengths are used to study the effectiveness of evolutionary programming (EP) as a general approach for finding SEMs for edit metric decoding. Direct and fuzzy classification methods are compared while also changing some of the EP settings to observe how decoding accuracy is affected. Regardless of code length, the best results are found using the fuzzy classification methods. For codes of length 10, a maximum accuracy of up to 99.4% is achieved for distance 1 whereas distance 2 and 3 achieve up to 97.1% and 85.9%, respectively. The accuracy suffers for longer codes, as the maximum accuracies achieved by codes of length 14 were 92.4%, 85.7% and 69.2% for distance 1, 2, and 3 respectively. Additionally, the SEMs are examined for potential bloat by comparing the number of reachable states against the total number of states. Bloat is seen more in larger machines than it is in smaller machines. Furthermore, the results are analyzed to find potential trends and relationships among the parameters, with the most consistent trend being that, when allowed, the longer codes generally show a propensity for larger machines.