abstract = "We describe a system, ECStar, that outstrips many
scaling aspects of extant genetic programming systems.
One instance in the domain of financial strategies has
executed for extended durations (months to years) on
nodes distributed around the globe. ECStar system
instances are almost never stopped and restarted,
though they are resource elastic. Instead they are
interactively redirected to different parts of the
problem space and updated with up-to-date learning.
Their non-reproducibility (i.e. single play of the tape
process) due to their complexity makes them similar to
real biological systems. In this contribution we focus
upon how ECStar introduces a provocative, important,
new paradigm for GP by its sheer size and complexity.
ECStar's scale, volunteer compute nodes and distributed
hub-and-spoke design have implications on how a
multi-node instance is managed. We describe the set up,
deployment, operation and update of an instance of such
a large, distributed and long running system. Moreover,
we outline how ECStar is designed to allow manual
guidance and re-alignment of its evolutionary search