Traditional Genetic Algorithms (GA) use crossover and mutation as the main genetic operators to achieve population diversity. Previous work using a biologically inspired genetic operator called transposition, allowed the GA to reach better solutions by replacing the traditional crossover operators. In this paper we extend that work to the case of asexual reproduction. The GA efficiency was compared when using asexual transposition and the classical crossover operators. The results obtained show that asexual transposition still allowed the modified GA to achieve higher performances.
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