  We develop algorithms capable of tackling robust black-box optimisation
problems, where the number of model runs is limited. When a desired solution
cannot be implemented exactly the aim is to find a robust one, where the worst
case in an uncertainty neighbourhood around a solution still performs well.
This requires a local maximisation within a global minimisation.
  To investigate improved optimisation methods for robust problems, and remove
the need to manually determine an effective heuristic and parameter settings,
we employ an automatic generation of algorithms approach: Grammar-Guided
Genetic Programming. We develop algorithmic building blocks to be implemented
in a Particle Swarm Optimisation framework, define the rules for constructing
heuristics from these components, and evolve populations of search algorithms.
Our algorithmic building blocks combine elements of existing techniques and new
features, resulting in the investigation of a novel heuristic solution space.
  As a result of this evolutionary process we obtain algorithms which improve
upon the current state of the art. We also analyse the component level
breakdowns of the populations of algorithms developed against their
performance, to identify high-performing heuristic components for robust
problems.
