# On The Nature Of The Phenotype In Tree Genetic Programming

arXiv: https://arxiv.org/abs/2402.08011 · HTML: https://mlsanity.com/paper/2402.08011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Sharpness-Aware Minimization in Genetic Programming](https://mlsanity.com/paper/2405.10267/) (2024)
- [Node Preservation and its Effect on Crossover in Cartesian Genetic Programming](https://mlsanity.com/paper/2511.00634/) (2025)

## Abstract (arXiv)

In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five benchmark datasets. We show that TGP exhibits the same behavior that we can observe in other GP representations: At the genotypic level trees show frequently unchecked growth with seemingly ineffective code, but on the phenotypic level, much smaller trees can be observed. To generate phenotypes, we provide a unique technique for removing semantically ineffective code from GP trees. The approach extracts considerably simpler phenotypes while not being limited to local operations in the genotype. We generalize this transformation based on a problem-independent parameter that enables a further simplification of the exact phenotype by coarse-graining to produce approximate phenotypes. The concept of these phenotypes (exact and approximate) allows us to clarify what evolved solutions truly predict, making GP models considered at the phenotypic level much better interpretable.