Genetic Programming and Evolvable Machines

, Volume 16, Issue 4, pp 499–530 | Cite as

Prudent alignment and crossover of decision trees in genetic programming



Crossover is the central search operator responsible for navigating through unknown problem landscapes while at the same time the main conservation operator, which is supposed to preserve the already learned lessons. This paper is about a novel homologous decision tree crossover operator. Contrary to other tree crossover operators it defines the context for a decision tree node and elaborates a fast one-sample-based tree alignment procedure. The idea is to replace a sub-tree with a better one from the same context, as defined by the decision tree training process. This operator does not rely on the topological properties of the tree but rather on its behavioral properties. During empirical testing the new operator showed the best generalization capabilities.


Genetic programming Decision trees Crossover  Context Alignment 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

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