An investigation of fitness sharing with semantic and syntactic distance metrics

  • Authors:

  • Quang Uy Nguyen;Xuan Hoai Nguyen;Michael O'Neill;Alexandros Agapitos

  • Affiliations:

  • Faculty of Information Technology, Military Technical Academy, Vietnam;IT Research and Development Center, Hanoi University, Vietnam;Natural Computing Research & Applications Group, University College Dublin, Dublin, Ireland;Natural Computing Research & Applications Group, University College Dublin, Dublin, Ireland

  • Venue:
  • EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
  • Year:
  • 2012

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Abstract

This paper investigates the efficiency of using semantic and syntactic distance metrics in fitness sharing with Genetic Programming (GP). We modify the implementation of fitness sharing to speed up its execution, and used two distance metrics in calculating the distance between individuals in fitness sharing: semantic distance and syntactic distance. We applied fitness sharing with these two distance metrics to a class of real-valued symbolic regression. Experimental results show that using semantic distance in fitness sharing helps to significantly improve the performance of GP more frequently, and results in faster execution times than with the syntactic distance. Moreover, we also analyse the impact of the fitness sharing parameters on GP performance helping to indicate appropriate values for fitness sharing using a semantic distance metric.