论文标题
非平稳随机全局优化算法
Non-Stationary Stochastic Global Optimization Algorithms
论文作者
论文摘要
戈麦斯（Gomez）提出了一种表征随机全局优化算法的形式和系统的方法。使用它，Gomez用固定的下一步构造随机方法（即定义为固定马尔可夫过程的算法）形式化算法。这些是标准版本的爬山，平行爬山，世代遗传，稳态遗传和差异进化算法的情况。本文继续采用这种系统的形式方法。首先，我们将足够的条件从静止的马尔可夫工艺概括到了足够的条件。其次，我们为一些选择方案开发了马尔可夫内核。最后，我们使用系统的形式方法对模拟解放和进化形式进行形式化。
Gomez proposes a formal and systematic approach for characterizing stochastic global optimization algorithms. Using it, Gomez formalizes algorithms with a fixed next-population stochastic method, i.e., algorithms defined as stationary Markov processes. These are the cases of standard versions of hill-climbing, parallel hill-climbing, generational genetic, steady-state genetic, and differential evolution algorithms. This paper continues such a systematic formal approach. First, we generalize the sufficient conditions convergence lemma from stationary to non-stationary Markov processes. Second, we develop Markov kernels for some selection schemes. Finally, we formalize both simulated-annealing and evolutionary-strategies using the systematic formal approach.
