A Scalable Cellular Implementation of Parallel Genetic Programming

A new parallel implementation of genetic programming based on the
cellular model is presented and compared with both canonical
genetic programming and the island model approach. The method
adopts a load balancing policy that avoids the unequal utilization
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.