This article studies the sub-tree operators: mutation and crossover, within
the context of Genetic Programming. Two standard problems, symbolic linear
regression and a non-linear tree, were presented to the algorithm at each stage.
The behaviour of the operators in regard to fitness is first established, followed
by an analysis of the most optimal ratio between crossover and mutation.
Subsequently, three algorithms are presented as candidates to dynamically
learn the most optimal level of this ratio. The results of each algorithm are
then compared to each other and the traditional constant ratio.
Munroe, D.R. (2004), Genetic programming: the ratio of crossover to mutation as a function of time, Research Letters in the Information and Mathematical Sciences, 6, 83-96