Towards Advanced Phenotypic Mutations in Cartesian Genetic Programming
This work addresses a specific bottleneck in evolutionary computation for researchers, but it is incremental as it builds on existing methods with modest gains.
The paper tackled the limited genetic operators in Cartesian Genetic Programming by proposing two phenotypic mutation techniques inspired by biological evolution, resulting in improved search performance on symbolic regression and boolean functions problems.
Cartesian Genetic Programming is often used with a point mutation as the sole genetic operator. In this paper, we propose two phenotypic mutation techniques and take a step towards advanced phenotypic mutations in Cartesian Genetic Programming. The functionality of the proposed mutations is inspired by biological evolution which mutates DNA sequences by inserting and deleting nucleotides. Experiments with symbolic regression and boolean functions problems show a better search performance when the proposed mutations are in use. The results of our experiments indicate that the use of phenotypic mutations could be beneficial for the use of Cartesian Genetic Programming.