Abstract
A new parallel implementation of genetic programming
(GP) based on the cellular model is presented and
compared with both canonical GP and the island model
approach. The method adopts a load-balancing policy
that avoids the unequal use of the processors.
Experimental results on benchmark problems of different
complexity show the superiority of the cellular
approach with respect to the canonical sequential
implementation and the island model. A theoretical
performance analysis reveals the high scalability of
the implementation realized and allows to predict the
size of the population when the number of processors
and their efficiency are fixed.
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