Feature Based Algorithm Configuration: A Case Study with Differential Evolution
Feature Based Algorithm Configuration: A Case Study with Differential Evolution
复制标题
基于特征的算法配置：差分进化案例研究
DOI：
10.1007/978-3-319-45823-6_15
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发表时间：
2016
期刊：
影响因子：
4.1
通讯作者：
Marc Schoenauer
中科院分区：
文献类型：
--
作者：
Nacim Belkhir;Johann Dréo;P. Savéant;Marc Schoenauer
算法配置仍然是一个复杂的问题在这里，BBOB测试台的原始版本是通过学习经验性能来实现的。从问题特征和算法参数中，可以使用此性能模型来计算从特征值的经验最佳参数配置。设置优化测试台上的鲁棒性。
Algorithm Configuration is still an intricate problem especially in the continuous black box optimization domain. This paper empirically investigates the relationship between continuous problem features (measuring different problem characteristics) and the best parameter configuration of a given stochastic algorithm over a bench of test functions — namely here, the original version of Differential Evolution over the BBOB test bench. This is achieved by learning an empirical performance model from the problem features and the algorithm parameters. This performance model can then be used to compute an empirical optimal parameter configuration from features values. The results show that reasonable performance models can indeed be learned, resulting in a better parameter configuration than a static parameter setting optimized for robustness over the test bench.
影响因子：
14.4
作者：
Hutter, Frank;Xu, Lin;Leyton-Brown, Kevin
通讯作者：
Leyton-Brown, Kevin