Genetic Programming and Evolvable Machines

, Volume 16, Issue 3, pp 241–281 | Cite as

Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification

  • Rodrigo C. BarrosEmail author
  • Márcio P. Basgalupp
  • André C. P. L. F. de Carvalho


In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.


Hyper-heuristics Decision trees Fitness function  Imbalanced data 



This work was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Project 2009/14325-3.

Supplementary material

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Supplementary material 1 (xlsx 138 KB)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Rodrigo C. Barros
    • 1
    Email author
  • Márcio P. Basgalupp
    • 2
  • André C. P. L. F. de Carvalho
    • 3
  1. 1.Faculdade de Informática (FACIN)Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)Porto AlegreBrazil
  2. 2.Instituto de Ciência e Tecnologia (ICT)Universidade Federal de São Paulo (UNIFESP)São José dos CamposBrazil
  3. 3.Instituto de Ciências Matemáticas e de Computação (ICMC)Universidade de São Paulo (USP)São CarlosBrazil

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