Multifidelity and multioutput optimisation algorithms are an area of current interest in many areas of computational design as they allow experimental and computational proxies to be used intelligently in the search for optimal species. Characterisation of these algorithms involves benchmarks that typically either use analytic functions or existing multifidelity datasets. Unfortunately, existing analytic functions are often not representative of relevant problems, while many existing datasets are not constructed to easily allow systematic investigation of the influence of characteristics of the contained proxies functions. To fulfil this need, we present a methodology for systematic generation of synthetic fidelities derived from a reference ground truth function with a controllable degree of correlation.
翻译:多方和多输出优化算法是目前许多计算设计领域关注的一个领域,因为这些算法允许在寻找最佳物种时明智地使用实验和计算代理物。这些算法的特性涉及通常使用分析函数或现有的多方真伪数据集的基准。不幸的是,现有的分析功能往往不能代表相关问题,而许多现有数据集的构建并不容易对包含的代理物功能的特性的影响进行系统调查。为了满足这一需要,我们提出了一个系统生成合成真实性的方法,该方法来自参照地面真理函数,具有可控制程度的关联性。