Accurate spam filters are of high necessity in present days
as the high amount of commercial mail entering accounts has become
a real threat to everyone, from causing personal computers to crash to
costing big companies billions of dollars annually because of employees
loss of productivity. Moreover, lately, spam also carries viruses along.
Current paper presents an evolutionary model of a spam filter which
can also be generalized to the more complex issue of text categorization.
The model learns the rules that lie behind the classification of training
e-mails into spam and non-spam and then uses them to label unseen,
incoming mail. The evolutionary learning classifier system uses genetic
chromodynamics to evolve the rule set. A comparison of its correctness
of prediction depending on whether chromosomes representing rules are
binary or real encoded is conducted. Experimental results showed that
the binary encoding appeared to be less effective than the real one.