灰盒优化和因子分解分布算法：两个世界碰撞的地方
Gray-box optimization and factorized distribution algorithms: where two worlds collide
摘要：
灰箱优化的概念与黑箱优化并列，围绕着利用问题结构来实现更有效的进化算法（EA）的想法。因子分解分布算法（FDAs）的工作，其因子分解直接来自问题结构，也有助于显示如何利用问题结构产生重要收益的效率EA。在本文中，我们分析了使用问题结构在进化算法中的一般问题，重点是面对灰箱优化所做的工作与相关的研究在FDAs完成。这种对比分析有助于我们确定，在目前的研究中使用的问题结构的EA，这些算法的工作原理的两个不同的分析特征。此外，我们声称，这两个特征的碰撞和竞争时，提供了一个连贯的框架来调查这种类型的算法。为了说明这一点，我们提出了一个对比分析的形式主义，问题，并在FDAs和灰箱优化产生的结果。共同的基本原则，这两种方法，通常被忽视，确定和讨论。此外，广泛的审查以前的研究有关的不同用途的问题结构在EA。本文还阐述了一些问题时出现的扩展使用的问题结构的EA，如问题的演化，高基数的变量和大的定义集，约束和多目标的问题，等。最后，紧急的方法，利用神经模型来捕捉问题的结构。
英文摘要：
The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves about the idea of exploiting the problem structure to implement more efficient evolutionary algorithms (EAs). Work on factorized distribution algorithms (FDAs), whose factorizations are directly derived from the problem structure, has also contributed to show how exploiting the problem structure produces important gains in the efficiency of EAs. In this paper we analyze the general question of using problem structure in EAs focusing on confronting work done in gray-box optimization with related research accomplished in FDAs. This contrasted analysis helps us to identify, in current studies on the use problem structure in EAs, two distinct analytical characterizations of how these algorithms work. Moreover, we claim that these two characterizations collide and compete at the time of providing a coherent framework to investigate this type of algorithms. To illustrate this claim, we present a contrasted analysis of formalisms, questions, and results produced in FDAs and gray-box optimization. Common underlying principles in the two approaches, which are usually overlooked, are identified and discussed. Besides, an extensive review of previous research related to different uses of the problem structure in EAs is presented. The paper also elaborates on some of the questions that arise when extending the use of problem structure in EAs, such as the question of evolvability, high cardinality of the variables and large definition sets, constrained and multi-objective problems, etc. Finally, emergent approaches that exploit neural models to capture the problem structure are covered.
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