  Typical artificial immune system (AIS) operators such as hypermutations with
mutation potential and ageing allow to efficiently overcome local optima from
which evolutionary algorithms (EAs) struggle to escape. Such behaviour has been
shown for artificial example functions constructed especially to show
difficulties that EAs may encounter during the optimisation process.
{\color{black}However, no evidence is available indicating that these two
operators have similar behaviour also in more realistic problems.} In this
paper we perform an analysis for the standard NP-hard \partition problem from
combinatorial optimisation and rigorously show that hypermutations and ageing
allow AISs to efficiently escape from local optima where standard EAs require
exponential time. As a result we prove that while EAs and random local search
(RLS) may get trapped on 4/3 approximations, AISs find arbitrarily good
approximate solutions of ratio (1+$\epsilon$) {\color{black}within $n(\epsilon
^{-(2/\epsilon)-1})(1-\epsilon)^{-2} e^{3} 2^{2/\epsilon} + 2n^3 2^{2/\epsilon}
+ 2n^3$ function evaluations in expectation. This expectation is polynomial in
the problem size and exponential only in $1/\epsilon$}.
