Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming

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. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms for robust problems. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. 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.

Automated summary

The study develops algorithms to tackle robust black-box optimization problems with limited model runs. By employing an automatic generation of algorithms approach, a novel heuristic solution space is investigated, resulting in algorithms that improve upon current state of the art. The component level breakdowns of the populations of algorithms developed are also analyzed to identify high-performing heuristic components for robust problems.