abstract = "CGP paper s report that crossover operators hinder CGP
search compared to a 1 + L strategy based on mutation
only. Though there have been efforts in making CGP
crossover operators work. This contrasts with what
happens in Linear Genetic Programming (LGP), where we
know that crossover works well. While both CGP and LGP
individuals can be represented as directed acyclic
graphs (DAGs), changing a single connection gene in a
CGP individual can drastically alter the activeness of
nodes in the entire graph, as opposed to LGP where
crossover changes are much more beneficial. we
demonstrate this and show that LGP evolution produces
children that are far more similar to their parents
than in CGP. This lets us propose that the design of
LGP, namely the inclusion of steady-state memory
registers and program size regulation, serves to
protect highfitness substructures from perturbation in
a way that is not provided for in CGP.",
notes = "Python3, fitness ~ Pearson correlation coefficient
Symbolic regresion: Koza-1, Koza-2, Koza-3, Nguyen-4,
Nguyen-5, Nguyen-6, Nguyen-7. 'calculation registers'.
'Similarity between best parents and best
children'.
Linear GP registers are used as anchor points. Variable
length is important.