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.

我们开发了能够解决鲁棒的黑盒优化问题的算法，而模型运行的次数是有限的。当无法完全实现所需的解决方案时，目标是找到一个可靠的解决方案，其中在解决方案周围不确定性邻域中最坏的情况仍然可以正常执行。为了研究改进的方法，我们采用了自动生成算法的方法：语法指导的遗传编程。我们在粒子群优化框架中开发算法构建块，从这些组件定义构造启发式的规则，并发展针对健壮问题的搜索算法群体。我们的算法构建块结合了现有技术和新功能的要素，从而研究了一种新颖的启发式解决方案空间。我们获得了改进现有技术水平的算法。我们还分析了针对其性能而开发的算法群体的组件级别细目分类，以确定针对鲁棒问题的高性能启发式组件。