A new parallel implementation of genetic programming based on the<BR>cellular model is presented and compared with both canonical<BR>genetic programming and the island model approach. The method<BR>adopts a load balancing policy that avoids the unequal utilization<BR>of the processors. Experimental results on benchmark problems of<BR>different complexity show the superiority of the cellular approach<BR>with respect to the canonical sequential implementation and the<BR>island model. A theoretical performance analysis reveals the high<BR>scalability of the implementation realized and allows to predict<BR>the size of the population when the number of processors and their<BR>efficiency are fixed.
A Scalable Cellular Implementation of Parallel Genetic Programming
Folino Gianluigi;Pizzuti Clara;Spezzano Giandomenico
2003
Abstract
A new parallel implementation of genetic programming based on thecellular 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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
