Mutation-based Genetic Algorithm: Performance Evaluation

The role of mutation has been frequently underestimated in the field of Evolutionary Computation. Moreover only little work has been done by researchers on mutations other than the classical point mutation. In fact, current versions of Genetic Algorithms (GAs) make use of this kind of mutation only, in spite of the existence in nature of many different forms of mutations. In this paper, we try to address these issues starting from the definition of two nature-based mutations, i.e. the frame-shift and the translocation. These mutation operators are applied to the solution of several test functions without making use of crossover. A comparison with the results achieved by classical crossover-based GAs, both sequential and parallel, shows the effectiveness of such operators.