Page History: Derivative-free optimization by classification
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For optimization non-covnex and complex functions, derivative-based methods may not effective because a point-wise derivative does not reflect the global landscape of the function. Instead, sampling in the solution space can reveal some global information about the function, and thus sampling-based methods, such as evolutionary algorithms, can be more suitable for complex optimizations.
RACOS
RACOS is designed according to the general complexity upper bound of a sampling-and-learning framework. It can be used to optimize functions in bounded continuous, discrete, and mixed solutions space. For details please see:
Yang Yu, Hong Qian, and Yi-Qi Hu. Derivative-free optimization via classification. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016. (
PDF) (
Appendix)
Java version (used in the experiments): (Code Download in Zip, KB)
Matlab version: coming soon...
C++ version: coming soon...
- The executing file are the AcrobotDemo.java, CWDemo.java, MountainCarDemo.java, and HelicopterHoverDemo.java in the
demo fold, each for a domain as by the name - The codes are released under the GNU GPL 2.0 license. For commercial purposes, please contact me or Prof. Zhi-Hua Zhou.