abstract = "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.",