# Node Preservation and its Effect on Crossover in Cartesian Genetic Programming

arXiv: https://arxiv.org/abs/2511.00634 · HTML: https://mlsanity.com/paper/2511.00634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 1 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Correlation versus RMSE Loss Functions in Symbolic Regression Tasks](https://mlsanity.com/paper/2205.15990/) (2022)
- [On The Nature Of The Phenotype In Tree Genetic Programming](https://mlsanity.com/paper/2402.08011/) (2024)
- [Analysing the Influence of Reorder Strategies for Cartesian Genetic Programming](https://mlsanity.com/paper/2410.00518/) (2024)

## Abstract (arXiv)

While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone (1+λ) evolutionary strategy has become the canonical approach for CGP. Although several operators have been developed that demonstrate an increased performance over the canonical method, a general solution to the problem is still lacking. In this paper, we compare basic crossover methods, namely one-point and uniform, to variants in which nodes are ``preserved,'' including the subgraph crossover developed by Roman Kalkreuth, the difference being that when ``node preservation'' is active, crossover is not allowed to break apart instructions. We also compare a node mutation operator to the traditional point mutation; the former simply replaces an entire node with a new one. We find that node preservation in both mutation and crossover improves search using symbolic regression benchmark problems, moving the field towards a general solution to CGP crossover.