Abstract
This paper presents a large and systematic body of
data on the relative effectiveness of mutation,
crossover, and combinations of mutation and crossover
in genetic programming (GP). The literature of
traditional genetic algorithms contains related
studies, but mutation and crossover in GP differ from
their traditional counterparts in significant ways. In
this paper we present the results from a very large
experimental data set, the equivalent of approximately
12,000 typical runs of a GP system, systematically
exploring a range of parameter settings. The resulting
data may be useful not only for practitioners seeking
to optimize parameters for GP runs, but also for
theorists exploring issues such as the role of
"building blocks" in GP.
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