用于快速组合黑箱优化的去噪自编码器
Denoising Autoencoders for fast Combinatorial Black Box Optimization
摘要：
分布估计算法（EDA）需要灵活的概率模型，可以有效地学习和采样。自动编码器（AE）是具有这些期望属性的生成随机网络。我们集成了一种特殊类型的AE，去噪自动编码器（DAE），到EDA和评估DAE-EDA的性能上的几个组合优化问题与一个单一的目标。我们评估适应度评估的数量以及所需的CPU时间。我们比较结果的性能贝叶斯优化算法（BOA）和RBM-EDA，另一种EDA是基于生成神经网络，已被证明具有竞争力的BOA。对于所考虑的问题实例，DAE-EDA比BOA和RBM-EDA快得多，有时是几个数量级。适应性评价的数量高于BOA，但与RBM-EDA竞争。这些结果表明，DAE可以是有用的工具，低，但不可忽略的健身评估成本的问题。
英文摘要：
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.
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