局部搜索的邻域相似适应度性质
The Neighbours' Similar Fitness Property for Local Search
摘要：
对于大多数实际的优化问题，局部搜索优于随机抽样-尽管有“没有免费的午餐定理”。本文介绍了搜索景观称为邻居的相似适应度（NSF）的基础上的良好性能的邻域搜索的局部改进的属性。虽然必要，但NSF不足以确保在好的解决方案的邻居中搜索改进比随机搜索更好。本文介绍了一个额外的（自然）属性，支持一般的证明，NSF景观，邻域搜索击败随机搜索。
英文摘要：
For most practical optimisation problems local search outperforms random sampling - despite the "No Free Lunch Theorem". This paper introduces a property of search landscapes termed Neighbours' Similar Fitness (NSF) that underlies the good performance of neighbourhood search in terms of local improvement. Though necessary, NSF is not sufficient to ensure that searching for improvement among the neighbours of a good solution is better than random search. The paper introduces an additional (natural) property which supports a general proof that, for NSF landscapes, neighbourhood search beats random search.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
