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This paper presents a computational methodology based on Genetic Algorithms with Genotype Editing (GAE) for investigating the role of RNA editing in dynamic environments. This model is based on genotype editing characteristics that are gleaned from RNA editing processes as observed in several organisms. We have previously expanded the traditional Genetic Algorithm (GA) with artificial editing mechanisms (Rocha, 1995, 1997), and studied the benefits of including straightforward Genotype Editing in GA for several machine learning problems (Huang and Rocha, 2003, 2004). Here we show that genotype editing also provides a means for artificial agents with genotype/phenotype mappings descriptions to gain greater phenotypic plasticity. We simulate agents endowed with the ability to alter the edition of their genotype according to environmental context. This ability grants agents an adaptive advantage as genotype expression can become contextually regulated. The study of this genotype edition model in changing environments has shed some light into the evolutionary implications of RNA editing. We expect that our methodology will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation and Artificial Life.