摘要翻译：
自分布估计算法(EDA)提出以来，人们一直试图在全局优化的背景下提高EDAS的性能。到目前为止，基于多变量概率模型的连续EDAs的研究和应用仍然局限于相当低维（小于100D)的问题。传统的EDAs由于维数的诅咒和快速增长的计算代价，难以解决高维问题。然而，扩展连续的EDAs用于高维优化仍然是必要的，这是由EDAs的独特特性所支持的：由于一个概率模型是显式估计的，从学习的模型中可以发现问题的有用性质或特征。除了获得一个很好的解，理解问题的结构可以有很大的好处，特别是对于黑箱优化。我们提出了一种新的具有模型复杂性控制的EDA框架（EDA-MCC）来扩展EDAS。通过弱因变量辨识(WI)和子空间建模(SM)的结合，EDA-MCC在高维问题上表现出明显优于传统EDAs的性能。此外，EDA-MCC还可以减少计算开销和对大种群规模的要求。除了能够找到一个好的解决方案，EDA-MCC还可以产生一个有用的问题结构表征。EDA-MCC是基于多元模型的EDAs的第一个成功实例，它可以有效地应用于多达500D的一般问题。它的性能也优于一些新开发的专门为大规模优化设计的算法。为了了解EDA-MCC的优缺点，我们对EDA-MCC进行了广泛的计算研究。我们的结果揭示了EDA-MCC在什么样的基准功能上可能优于其他功能。
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英文标题：
《Scaling Up Estimation of Distribution Algorithms For Continuous
  Optimization》
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作者：
Weishan Dong, Tianshi Chen, Peter Tino, and Xin Yao
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model based continuous EDAs are still restricted to rather low dimensional problems (smaller than 100D). Traditional EDAs have difficulties in solving higher dimensional problems because of the curse of dimensionality and their rapidly increasing computational cost. However, scaling up continuous EDAs for higher dimensional optimization is still necessary, which is supported by the distinctive feature of EDAs: Because a probabilistic model is explicitly estimated, from the learnt model one can discover useful properties or features of the problem. Besides obtaining a good solution, understanding of the problem structure can be of great benefit, especially for black box optimization. We propose a novel EDA framework with Model Complexity Control (EDA-MCC) to scale up EDAs. By using Weakly dependent variable Identification (WI) and Subspace Modeling (SM), EDA-MCC shows significantly better performance than traditional EDAs on high dimensional problems. Moreover, the computational cost and the requirement of large population sizes can be reduced in EDA-MCC. In addition to being able to find a good solution, EDA-MCC can also produce a useful problem structure characterization. EDA-MCC is the first successful instance of multivariate model based EDAs that can be effectively applied a general class of up to 500D problems. It also outperforms some newly developed algorithms designed specifically for large scale optimization. In order to understand the strength and weakness of EDA-MCC, we have carried out extensive computational studies of EDA-MCC. Our results have revealed when EDA-MCC is likely to outperform others on what kind of benchmark functions.
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PDF链接：
https://arxiv.org/pdf/1111.2221