I've been evaluating ECJ for possible use in a large scale cloud computing
based evolutionary computation project for the optimization of AIs in
highly complex wargames.

What makes this a hard problem is that:

1.  The evaluations are expensive - a mean of 400 seconds per evaluation on
a one core 3.5 ghz processor.
2.  The evaluations are noisy - a better AI can still lose to worse AI, and
often does
3.  The evaluation run times also have a large variance from approximately
80 seconds up to 1000 seconds.

As evolutionary approaches, I'm leaning to steady-state EDA type algorithms
as a seemingly good fit for the problem domain.

All was looking good in the evaluation of ECJ until what seems like a fatal
problem in the last sentence of section 6.1.6 Noisy Distributed Problems in
the ECJ Owners manual :

"There’s no equivalent to this hack in Asynchronous Evolution: you’ll just
have to ask a machine to test the individual 5 times."

Unfortunately that would seem to significantly reduce the ability to fan
out evaluations to reduce elapsed clock time per evaluation  which would
significantly increase "time travel" - ie where evaluated individuals
 re-enter a population as candidates for inclusion at a much later time
than they were created for evaluation.

Is another hack possible to spread out evaluations where one needs to run
multiple tests to get a good-enough estimator of an individual?   i might
even be willing to do the hacking.




-- 
Jim Rutt
JPR Ventures