Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming
研究在模型运行次数有限的情况下，自动生成算法来解决鲁棒黑箱优化问题，使用语法引导遗传编程构建算法模块，并进化出优于现有技术的搜索算法。
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.