Abstract
Evolutionary computation is a promising artificial
intelligence field involving the simulation of natural
evolution to solve problems. Given its implicit
parallelism and high computational requirements,
evolutionary computation is the perfect candidate for
high performance parallel computers. This paper
presents Distributed BEAGLE, a new master-slave
architecture for parallel and distributed evolutionary
computations. It is designed as a robust, adaptive, and
scalable system targeted for local networks of
workstations and Beowulf clusters. Results obtained
with a plausible deployment scenario demonstrate that
system performance degrades gracefully when failures
occurred, while still achieving near linear speedup in
the ideal case.
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