期刊
EVOLUTIONARY COMPUTATION
卷 20, 期 2, 页码 249-275出版社
MIT PRESS
DOI: 10.1162/EVCO_a_00069
关键词
Resampling; meta-models; model validation; regression; evolutionary optimization; evolutionary computation
资金
- Collaborative Research Center [SFB 823]
- Graduate School of Energy Efficient Production and Logistics
- Research Training Group Statistical Modelling of the German Research Foundation
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.
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