I was reading an article about how "it might be possible to improve the common decision trees (e.g. CART) using evolutionary heuristics such as the genetic algorithm"
"Commonly used classiﬁcation and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an eﬃcient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. "
I understand the basic logic in this argument : A decision tree that uses the genetic algorithm has the advantage of considering a much larger search space than the standard decision tree.
However, I was just wondering why in this article, regular decision trees are repeatedly described as "suboptimal"?
My Question: Does anyone know of any references which theoretically establish the weaknesses of standard decision trees with regards to the fact that they inherently will produce a suboptimal model?