Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression

  • Quang Uy Nguyen
  • Xuan Hoai Nguyen
  • Michael O’Neill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

In this paper, we apply the ideas from [2] to investigate the effect of some semantic based guidance to the crossover operator of GP. We conduct a series of experiments on a family of real-valued symbolic regression problems, examining four different semantic aware crossover operators. One operator considers the semantics of the exchanged subtrees, while the other compares the semantics of the child trees to their parents. Two control operators are adopted which reverse the logic of the semantic equivalence test. The results show that on the family of test problems examined, the (approximate) semantic aware crossover operators can provide performance advantages over the standard subtree crossover adopted in Genetic Programming.

Keywords

crossover semantic genetic programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Quang Uy Nguyen
    • 1
  • Xuan Hoai Nguyen
    • 2
  • Michael O’Neill
    • 1
  1. 1.Natural Computing Research & Applications GroupUniversity College DublinIreland
  2. 2.School of Computer Science and EngineeringSeoul National UniversityKorea

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