Towards Understanding Crossover for Cartesian Genetic Programming

Unlike in traditional Genetic Programming, Cartesian Genetic Programming (CGP) does not commonly feature a recombination/crossover operator, although recombination plays an important role in other evolutionary techniques, including Genetic Programming from which CGP originates. Instead, CGP mainly depends on mutation and selection operators in their evolutionary search. To this day, it is still unclear as to why CGP’s performance does not generally improve with the addition of crossover. In this work, we argue that CGP’s positional bias might be a reason for this phenomenon. This bias describes a skewed distribution of active and inactive nodes, which might lead to destructive behaviour of standard recombination operators. We provide a first assessment with preliminary results. No final conclusion to this hypothesis can be drawn yet, as more thorough evaluations must be done first. However, our first results show promising trends and may lay the foundationf or future work.