Symmetry at the Genotypic Level and the Simple Inversion Operator

Classical Genetic Algorithm theory was built on four operators: proportional selection, one-point crossover, mutation and inversion. While the role of inversion was questioned, the use of the other remaining operators has thrived, some of these newly designed operators being motivated by good empirical results, some having a solid theory to support their use. In this paper we present a Simple Inversion Operator, and we investigate its potential mixing capabilities for problems where the optimum consists of juxtaposed Symmetric Building Blocks. Both theoretical investigation and experimental results obtained, indicate that our operator is quite powerful in finding the right building blocks that compose the optimum, whenever symmetrical building blocks play an important role in the discovery of the global solution.

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