A Generic Framework for Building Dispersion Operators in the Semantic Space

  • Luiz Otavio V. B. Oliveira
  • Fernando E. B. Otero
  • Gisele L. Pappa
Part of the Genetic and Evolutionary Computation book series (GEVO)


This chapter proposes a generic framework to build geometric dispersion (GD) operators for Geometric Semantic Genetic Programming in the context of symbolic regression, followed by two concrete instantiations of the framework: a multiplicative geometric dispersion operator and an additive geometric dispersion operator. These operators move individuals in the semantic space in order to balance the population around the target output in each dimension, with the objective of expanding the convex hull defined by the population to include the desired output vector. An experimental analysis was conducted in a testbed composed of sixteen datasets showing that dispersion operators can improve GSGP search and that the multiplicative version of the operator is overall better than the additive version.


Genetic programming Geometric semantic genetic programming Dispersion operators Behavioral diversity Symbolic regression Search operators 



The authors would like to thank CAPES, FAPEMIG, and CNPq (141985/ 2015-1) for their financial support.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Luiz Otavio V. B. Oliveira
    • 1
  • Fernando E. B. Otero
    • 2
  • Gisele L. Pappa
    • 1
  1. 1.DCC, Universidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.School of ComputingUniversity of KentChatham MaritimeUK

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