  This paper presents a competent selectomutative genetic algorithm (GA), that
adapts linkage and solves hard problems quickly, reliably, and accurately. A
probabilistic model building process is used to automatically identify key
building blocks (BBs) of the search problem. The mutation operator uses the
probabilistic model of linkage groups to find the best among competing building
blocks. The competent selectomutative GA successfully solves additively
separable problems of bounded difficulty, requiring only subquadratic number of
function evaluations. The results show that for additively separable problems
the probabilistic model building BB-wise mutation scales as O(2^km^{1.5}), and
requires O(k^{0.5}logm) less function evaluations than its selectorecombinative
counterpart, confirming theoretical results reported elsewhere (Sastry &
Goldberg, 2004).
