abstract = "Solving complex, real-world problems with genetic
programming (GP) can require extensive computing
resources. However, the highly parallel nature of GP
facilitates using a large number of resources
simultaneously, which can significantly reduce the
elapsed wall clock time per GP run. This paper explores
the performance characteristics of an MPI version of
the Genetic Programming Environment for FIFTH (GPE5) on
a high performance computing cluster. The
implementation is based on the island model with each
node running the GP algorithm asynchronously. In
particular, we examine the effect of several
configurable properties of the system including the
ratio of migration to crossover, the migration cycle of
programs between nodes, and the number of processors
used. The problems employed in the study were selected
from the fields of symbolic regression, finite algebra,
and digital signal processing.",
notes = "GECCO-2009 A joint meeting of the eighteenth
international conference on genetic algorithms
(ICGA-2009) and the fourteenth annual genetic
programming conference (GP-2009).