A particle filtering framework for randomized optimization algorithms

We propose a framework for optimization problems based on particle filtering (also called Sequential Monte Carlo method). This framework unifies and provides new insight into randomized optimization algorithms. The framework also sheds light on developing new optimization algorithms through the freedom in the framework and the various improving techniques for particle filtering.

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