Faculty of Statistics, TU Dortmund University, Dortmund, Germany.
Evol Comput. 2012 Summer;20(2):249-75. doi: 10.1162/EVCO_a_00069. Epub 2012 Mar 12.
Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.
元建模已成为解决昂贵优化问题的关键工具。过去的大部分工作都集中在寻找一种好的回归方法来对适应度函数进行建模。例如，包括经典线性回归、样条函数、神经网络、克里金和支持向量回归。本文特别提请注意，评估模型准确性是元建模框架中的一个关键方面。例如交叉验证、子采样、引导和嵌套重采样等重采样策略是模型验证的突出方法，并系统地讨论了它们可能存在的陷阱、缺点和具体特点。本文提供了进化优化中元建模技术的调查。此外，还给出了一些说明与模型选择和性能评估相关的陷阱的实际示例。最后，为特定环境下选择模型验证技术提出了建议。
