Related Experiment Video
Updated: May 15, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
Automatic design of decision-tree algorithms with evolutionary algorithms
Rodrigo C Barros1, Márcio P Basgalupp, André C P L F de Carvalho
1Universidade de São Paulo, São Carlos, Brazil rcbarros@icmc.usp.br.
Abstract:
This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.
Related Concept Videos
Survival Tree
Building a Survival Tree
Constructing a survival tree begins...
Phylogenetic Trees
Phylogenetic Trees
Evolutionary Relationships through Genome Comparisons
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Limits to Natural Selection
