genetic algorithms for crypto analysis

[Paul Baclace]

Using a GA to drive a brute force key search would certainly not help:
the fitness surface has a needle in a haystack (spike).  This kind of
problem has been identified as "unlearnable" by theoreticians like 
Valiant at Harvard.

However, using a GA to drive a more intelligent cryptanalysis that has
partial results *would* help.  It seems that cryptanalysis benefits from 
human assistance due our excellent abilities at recognition of partial
solutions.  Because of this, a GA could help automate the cryptanalysis
process.

(My knowledge of cryptanalysis ends at the Enigma machine breaker cbw
(crypt breakers workbench in comp.sources.unix archives) which uses an
interative process where partial results are visible and are used to guide
new guesses.  The Enigma machine does state-machine substitution, but 
no diffusion/mixing/scrambling; lack of the latter makes visual 
recognition much simpler.  Since DES uses scrambling, I'm not sure whether
partial results are possible.)

>  I recall reading (I think in Sci. Am.) that a theory under investigation
>  now as to why nature has sexual reproduction as part of its repertoire
>  is that this gives a solution-seeking population a better opportunity to
>  located spikey solutions.

Crossover of the genome is the key part of "avoiding hill-climbing" and it the 
key ingredient to Holland's proof of a super-linear speedup (thus "violating"
Amdahl's Law of parallelization never attaining a linear speedup) otherwise 
known as implicit parallelism.  Holland's proof of this in the '70s opened 
up research in GAs because of this attractive feature.  [Note that it requires
certain assumptions about independence and stasis of the bits in the genome
to make the proof tractable, but the hope is that this will still be useful
for real problems.]

Paul E. Baclace
peb at procase.com