摘要翻译：
本文介绍了健身云的概念，作为一种可视化和分析搜索空间的替代方法，而不是健身景观的地理概念。认为适合度云概念克服了景观表示的几个不足。我们的分析是基于解的适应度与最近解的适应度之间的相关性。我们重点研究了在著名的NK适应度景观上的局部搜索启发式的行为，如hill climber。在这两种情况下，适应度对适应度的相关性被显示为与上位性参数k相关。
---
英文标题：
《Local search heuristics: Fitness Cloud versus Fitness Landscape》
---
作者：
Philippe Collard (I3S), S\'ebastien Verel (I3S), Manuel Clergue (I3S)
---
最新提交年份：
2007
---
分类信息：

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：
  This paper introduces the concept of fitness cloud as an alternative way to visualize and analyze search spaces than given by the geographic notion of fitness landscape. It is argued that the fitness cloud concept overcomes several deficiencies of the landscape representation. Our analysis is based on the correlation between fitness of solutions and fitnesses of nearest solutions according to some neighboring. We focus on the behavior of local search heuristics, such as hill climber, on the well-known NK fitness landscape. In both cases the fitness vs. fitness correlation is shown to be related to the epistatic parameter K.
---
PDF链接：
https://arxiv.org/pdf/0709.4010