Phenotypic duplication and inversion in cartesian genetic programming applied to boolean function learning

The search performance of Cartesian Genetic Programming (CGP) relies to a large extent on the sole use of genotypic point mutation in combination with extremely large redundant genotypes. Over the last years, steps have been taken to extend CGP's variation mechanisms by the introduction of advanced methods for recombination and mutation. One branch of these contributions addresses phenotypic variation in CGP. However, recent studies have demonstrated the limitations of phenotypic recombination in Boolean function learning and highlighted the effectiveness of the mutation-only approach. Therefore, in this work, we further explore phenotypic mutation in CGP by the introduction and evaluation of two phenotypic mutation operators that are inspired by chromosomal rearrangement. Our initial findings show that the proposed methods can significantly improve the search performance of CGP on various single- and multiple-output Boolean function benchmarks.