Editing Genetic programming (section)

===Crossover===
[[File:Genetic_programming_subtree_crossover.gif|thumb|Genetic programming subtree crossover]]
In Genetic Programming two fit individuals are chosen from the population to be parents for one or two children. In tree genetic programming, these parents are represented as inverted lisp like trees, with their root nodes at the top. In subtree crossover in each parent a subtree is randomly chosen. (Highlighted with yellow in the animation.) In the root donating parent (in the animation on the left) the chosen subtree is removed and replaced with a copy of the randomly chosen subtree from the other parent, to give a new child tree.

Sometimes two child crossover is used, in which case the removed subtree (in the animation on the left) is not simply deleted but is copied to a copy of the second parent (here on the right) replacing (in the copy) its randomly chosen subtree. Thus this type of subtree crossover takes two fit trees and generates two child trees.

The tree-based approach in Genetic Programming also shares structural and procedural similarities with earlier knowledge-based and topology-oriented crossover methods. Specifically, analogous crossover and homologous crossover, both implemented in robot trajectory planning, exhibit a resemblance to subtree operations in tree GP. These crossover mechanisms were described in the context of heuristic optimisation strategies in robotics.<ref>Davidor, Y. (1991). Genetic Algorithms and Robotics: A Heuristic Strategy for Optimization. World Scientific Series in Robotics and Intelligent Systems: Volume 1.</ref>