Page History: Model-based Derivative-free Methods for Optimization
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Page Revision: 2016/04/21 16:44
Derivative-free methods can tackle complex optimizations in real domains, such as non-convex, non-differentiable, and non-continuous problems with many local optima.
Papers:
- General analysis: In the CEC'14 (PDF) paper, we proposed the sampling-and-learning (SAL) framework to capture the essence of model-based optimization algorithms, and analyzed its performance using the query complexity for achieving approximate solutions with a probability. We derived a general query complexity bound for SAL algorithms where the learning model is a classifier.
- Classification-based optimization: In the AAAI'16 (PDF) (Appendix) paper, we discovered key factors for classification-based optimization methods, and designed the RACOS algorithm accordingly. RACOS has been shown superior to some state-of-the-art derivative-free optimization algorithms.
- Classification-based optimization in discrete domains: In the CEC'16 (PDF) paper, we analyzed the performance of the classification-based optimization in finite discrete spaces.
Codes:
- The derivative-free optimization by classification algorithm: RACOS