Cooling Strategies for the Moment-Generating Function in Bayesian Global Optimization
By Hao Wang, Michael T. M. Emmerich, Thomas Bäck. 2018
21 citations recorded by the source corpus.
Bayesian Global Optimization algorithm is designed to optimize expensive objective functions with small evaluation budget. This algorithm employs a surrogate model and assesses the potential improvement of unseen solutions through the so-called infill-criterion. A novel infill-criterion proposed in our previous work is derived from the moment-generating function of the improvement. In contrast to other techniques, it features a continuous parameter that can be used to adjust the exploration-exploitation tradeoff smoothly. In this work, two cooling strategies (linear and exponential) are adopted to enhance the explorative behavior in the early stage of the search and the exploitative effect in the final converging stage. Moreover, the initial temperature and cooling speed are investigated on some selected multi-modal functions, showing that the good setting of those two parameters depends on the problems specifics. The proposed Bayesian optimization with cooling strategy is tested on well-known BBOB benchmark. The results shows that without tuning the initial temperature and cooling speed, the proposed approach improves the performance on a range of multi-modal functions as compared to the commonly used expected improvement criterion.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
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In Artificial Intelligence
- Andrés Camero
- Enrique Alba
- Thomas Bäck
- Zwe-Lee Gaing
- Jamal Toutouh
- Jun Zhang
- Zhi-hui Zhan
- Yi-Tung Kao
In Computational Theory and Mathematics
In Management Science and Operations Research
Citations in the corpus
- Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, methodology): “We implemented our approach1 in Python 3, using LOPT [39], MIP-EGO [40], Keras [41], and Tensorflow [42].”