Abstract
This paper presents a scalable parallel implementation
of genetic programming on distributed memory machines.
The system runs multiple master-slave instances each
mapped on all the allocated nodes and multithreading is
used to overlap message latencies with useful
computation. Load balancing is achieved using a dynamic
scheduling algorithm and comparison with a static
algorithm is reported. To alleviate premature
convergence, asynchronous migration of individuals is
performed among processes. We show that nearly linear
speedups can be obtained for problems of large enough
size. The system has been applied to infer robust
trading strategies which is a compute-intensive
financial application. Copyright 2001, Elsevier
Science, All rights reserved.
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