  There is considerable interest in the use of genetic algorithms to solve
problems arising in the areas of scheduling and timetabling. However, the
classical genetic algorithm paradigm is not well equipped to handle the
conflict between objectives and constraints that typically occurs in such
problems. In order to overcome this, successful implementations frequently make
use of problem specific knowledge. This paper is concerned with the development
of a GA for a nurse rostering problem at a major UK hospital. The structure of
the constraints is used as the basis for a co-evolutionary strategy using
co-operating sub-populations. Problem specific knowledge is also used to define
a system of incentives and disincentives, and a complementary mutation
operator. Empirical results based on 52 weeks of live data show how these
features are able to improve an unsuccessful canonical GA to the point where it
is able to provide a practical solution to the problem
