Abstract
In this paper we study and compare the search
properties of different crossover operators in genetic
programming (GP) using probabilistic models and
experiments to assess the amount of genetic material
exchanged between the parents to generate the
offspring. These operators are: standard crossover,
one-point crossover and a new operator, uniform
crossover. Our analysis suggests that standard
crossover is a local and biased search operator not
ideal to explore the search space of programs
effectively. One-point crossover is better in some
cases as it is able to perform a global search at the
beginning of a run, but it suffers from the same
problems as standard crossover later on. Uniform
crossover largely overcomes these limitations as it is
global and less biased.
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