The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

  • Julio Albinati
  • Gisele L. Pappa
  • Fernando E. B. Otero
  • Luiz Otávio V. B. Oliveira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9025)


This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.


Semantic genetic programming Crossover Crossover mask optimization 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Julio Albinati
    • 1
  • Gisele L. Pappa
    • 1
  • Fernando E. B. Otero
    • 2
  • Luiz Otávio V. B. Oliveira
    • 1
    • 3
  1. 1.Universidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Chatham MaritimeUniversity of KentKentUK
  3. 3.Instituto Federal Do Sul de Minas GeraisPoços de CaldasBrazil

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