论文标题
邻居的类似健身属性用于本地搜索
The Neighbours' Similar Fitness Property for Local Search
论文作者
论文摘要
对于大多数实用的优化问题，尽管“无免费午餐定理”，本地搜索的表现优于随机抽样。本文介绍了搜索景观的属性，称为邻居的类似健身（NSF），这是基于邻里搜索的良好表现，从本地改进来看。尽管有必要，但NSF不足以确保在良好解决方案的邻居中搜索改进比随机搜索更好。该论文引入了一个额外的（自然）属性，该属性支持了一般证明，即对于NSF景观，邻里搜索比较随机搜索。
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.
