Tournament Selection Based on Statistical Test in Genetic Programming

  • Thi Huong Chu
  • Quang Uy NguyenEmail author
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)


Selection plays a critical role in the performance of evolutionary algorithms. Tournament selection is often considered the most popular techniques among several selection methods. Standard tournament selection randomly selects several individuals from the population and the individual with the best fitness value is chosen as the winner. In the context of Genetic Programming, this approach ignores the error value on the fitness cases of the problem emphasising relative fitness quality rather than detailed quantitative comparison. Subsequently, potentially useful information from the error vector may be lost. In this paper, we introduce the use of a statistical test into selection that utilizes information from the individual’s error vector. Two variants of tournament selection are proposed, and tested on Genetic Programming for symbolic regression problems. On the benchmark problems examined we observe a benefit of the proposed methods in reducing code growth and generalisation error.


Genetic Programming Tournament selection Statistical test 



This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2014.09. MON acknowledges the support of Science Foundation Ireland grant 13/IA/1850.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Thi Huong Chu
    • 1
  • Quang Uy Nguyen
    • 1
    Email author
  • Michael O’Neill
    • 2
  1. 1.Faculty of ITLe Quy Don Technical UniversityHanoiVietnam
  2. 2.Natural Computing Research and Applications Group, UCD BusinessUniversity College DublinDublinIreland

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