摘要翻译：
本文提出了一种新的方法来研究硬组合问题的构形空间的结构。它包括建立具有局部最优配置的节点和吸引盆地之间的加权定向转移的边缘的网络。我们将该方法应用于由两类不同的硬组合优化问题：二次指派问题(QAP)的实例所产生的最优网络中的社区检测。我们提供的证据表明，这两个问题实例类产生了非常不同的配置空间。对于所谓的真类，网络具有清晰的模块结构，而属于随机一致实例类的最优网络则不太容易划分成簇。这是令人信服的支持使用几个统计测试。最后，我们简要地讨论了这些发现对于启发式搜索相应问题空间的结果。
---
英文标题：
《Communities of Minima in Local Optima Networks of Combinatorial Spaces》
---
作者：
Fabio Daolio (ISI), Marco Tomassini (ISI), S\'ebastien Verel (INRIA
  Lille - Nord Europe), Gabriela Ochoa
---
最新提交年份：
2012
---
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。
--
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。
--

---
英文摘要：
  In this work we present a new methodology to study the structure of the configuration spaces of hard combinatorial problems. It consists in building the network that has as nodes the locally optimal configurations and as edges the weighted oriented transitions between their basins of attraction. We apply the approach to the detection of communities in the optima networks produced by two different classes of instances of a hard combinatorial optimization problem: the quadratic assignment problem (QAP). We provide evidence indicating that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the networks possess a clear modular structure, while the optima networks belonging to the class of random uniform instances are less well partitionable into clusters. This is convincingly supported by using several statistical tests. Finally, we shortly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.
---
PDF链接：
https://arxiv.org/pdf/1207.4445