> Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as selection, crossover, and mutation.
Rosalind.info has free CS algorithms applied bioinformatics exercises in Python; in a tree or a list; including genetic combinatorics.
https://rosalind.info/problems/list-view/
FWICS there is not a "GA with code exercise" in the AP Bio or Rosalind curricula.
YouTube has videos of simulated humanoids learning to walk with mujoco and genetic algorithms that demonstrate goal-based genetic programming with Cost / Error / Fitness / Survival functions.
Mutating source code AST is a bit different from mutating to optimize a defined optimization problem with specific parameters; though the task is basically the same: minimize error between input and output, and then XAI.
> Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as selection, crossover, and mutation.
AP®/College Biology: https://www.khanacademy.org/science/ap-biology