论文标题
自动生成黑盒算法可靠的优化问题
Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems
论文作者
论文摘要
我们开发了能够解决强大的黑盒优化问题的算法，其中模型运行的数量有限。当无法实现所需的解决方案时，目的是找到一个健壮的解决方案，在该解决方案中，解决方案周围的不确定性社区中最坏的情况仍然表现良好。这需要在全球最小化中进行局部最大化。 为了研究改进的可靠问题的优化方法，并消除了手动确定有效的启发式和参数设置的需求，我们采用了自动生成的算法方法：语法引导的基因编程。我们开发了要在粒子群优化框架中实现的算法构建块，定义了从这些组件中构建启发式方法的规则，并进化了搜索算法的种群。我们的算法构建块结合了现有技术和新功能的元素，从而研究了新型的启发式解决方案空间。 由于这一进化过程，我们获得了算法，从而改善了当前最新水平的状态。我们还分析了针对其性能开发的算法种群的组件水平分解，以确定可靠问题的高性能启发式组件。
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.
