摘要翻译：
本文对最近提出的组合景观模型--局部最优网络(LON)进行了扩展，将最优最优爬山算法代替最优最优爬山算法，用于景观最优吸引池的定义和提取。在此基础上，本文提出了一种新的Lon-Optima Networks算法，即Lon-Optima Networks算法，即Lon-Optima Networks算法，即Lon-Optima Networks算法。对一组NK景观的最佳网络模型和第一次改进网络模型进行了统计分析，并进行了讨论。我们的结果表明，在网络连通性和吸引盆地的性质方面，两个模型之间存在结构差异。深入讨论了这些差异对基于第一和最佳改进局部搜索的搜索启发式行为的影响。
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英文标题：
《First-improvement vs. Best-improvement Local Optima Networks of NK
  Landscapes》
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作者：
Gabriela Ochoa, S\'ebastien Verel (INRIA Lille - Nord Europe), Marco
  Tomassini (ISI)
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最新提交年份：
2012
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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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英文摘要：
  This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.
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PDF链接：
https://arxiv.org/pdf/1207.4455