http://www.hughes-family.org/bugzilla/show_bug.cgi?id=1012
------- Additional Comments From easmith@beatrice.rutgers.edu  2002-09-21 16:41 -------
I like this idea, but from my reading - part of my dissertation research -
on GAs (or, in this case, more precisely "Evolutionary Programming", since
the parameters are non-binary and the major operator is mutation, not
crossover), you do need to keep in mind that the more interactions there are
between different variables, the better-tweaked the GA/EA will need to be,
especially to avoid local optima (which may have been the problem with the
suspiciously-high "anti-ratware" USER_AGENT scores). From past results,
what's needed are one or more of the below:
A. Determine mutational parameters by adapted-scores themselves,
with variation on a per-original score basis; ideally, allow
for having "correlated" mutations - in other words, have a
mechanism in place for trying out changes to a bunch of scores at
once, with them all moving about the same amount (albeit possibly
in different directions) - one means to do this is found in the
"Evolutionary Strategies" of Schwefel;
B. Adapt the probability of a mutation taking place depending on how
well previous mutation tries have done - if more than about a
fifth of the new "individuals" are doing about as well as, or
better than, the parental generation, then mutate more
parameters; if less than a fifth of them are doing about as well
as, or better than, the parental generation, then mutate less
parameters.
-Allen
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