  Heuristic optimisers which search for an optimal configuration of variables
relative to an objective function often get stuck in local optima where the
algorithm is unable to find further improvement. The standard approach to
circumvent this problem involves periodically restarting the algorithm from
random initial configurations when no further improvement can be found. We
propose a method of partial reinitialization, whereby, in an attempt to find a
better solution, only sub-sets of variables are re-initialised rather than the
whole configuration. Much of the information gained from previous runs is hence
retained. This leads to significant improvements in the quality of the solution
found in a given time for a variety of optimisation problems in machine
learning.
