Parallel genetic programming: an application to trading models evolution

We present a parallel implementation of genetic programming on distributed memory machines. To overcome the time overhead due to uneven load associated with program evaluation, we propose and evaluate a non-preemptive dynamic scheduling algorithm for load balancing. The system is applied to the evolution of trading model strategies which is a compute-intensive application. Our results show that reasonable trading models can be inferred and that the system can produce a nearly linear speedup for that application.