GSO群搜索优化算法(Group Search Optimizer)以及它的一个改进算法SGSO算法(Simplified Group Search Optimizer Algorithm)，可用于高维优化问题.

search and replace 6.7 破解汉化版，内附说明，亲测可以使用，希望对你有帮助。

An example to apply tabu search to find optimal routes for TSP prblem

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.

这是一本讲诉搜索开发技术的书，在亚马逊上评分为五星。众所周知，搜索引擎很多技术都很成熟了，但其实践的成本很高，因此，很多相关书籍讲解的大多是概念，而计算机科学的内核是实践，没法动手做的东西，意义都不大。本书特点在于它基于一个信息检索的开源系统Wumpus，使得理论和实践能够结合起来，这或许是它在亚马逊有五星的原因吧，希望有志于信息检索的各位同仁能充书里获得应有的收获

如果说 Actual Search & Replace 堪称文件内容替换工具中的 “屠龙刀”的话，那么，Search and Replace 则堪称文件内容搜索 工具中的“倚天剑”。它不仅可以在任何文件中搜索，甚至可以以 二进制或脚本方式搜索，在 ZIP 文件中搜索，可以以文本或网页方式 显示搜索结果，批量替换文件时间属性...总之，如果你要对文件内容 进行批处理搜索或替换的话，建议常备 Search and Replace 以及 Actual Search & Replace 这两个“倚天剑” 和 “屠龙刀”。个人认为，Search and Replace 侧重于搜索方面，Actual Search & Replace 侧重于替换方面，对于网站站长以及电子书制作者等来说，绝对是不可多得的利器！ 注: Search and Replace 新版增加了文件夹及文件右键菜单功能，使用更加方便。

spring spingmvc 集成elasticSearch 5.5.x版本 ，基本的增删改查.完成

布谷鸟算法python代码,基于xinshe Yang的matlab m文件改编

Part I Metric Searching in a Nutshell Overview 3 1. FOUNDATIONS OF METRIC SPACE SEARCHING 5 1 The Distance Searching Problem 6 2 The Metric Space 8 3 Distance Measures 9 3.1 Minkowski Distances 10 3.2 Quadratic Form Distance 11 3.3 Edit Distance 12 3.4 Tree Edit Distance 13 3.5 Jaccard’s Coefficient 13 3.6 Hausdorff Distance 14 3.7 Time Complexity 14 4 Similarity Queries 15 4.1 Range Query 15 4.2 Nearest Neighbor Query 16 4.3 Reverse Nearest Neighbor Query 17 4.4 Similarity Join 17 4.5 Combinations of Queries 18 4.6 Complex Similarity Queries 18 5 Basic Partitioning Principles 20 5.1 Ball Partitioning 20 5.2 Generalized Hyperplane Partitioning 21 5.3 Excluded Middle Partitioning 21 5.4 Extensions 21 6 Principles of Similarity Query Execution 22 6.1 Basic Strategies 22 6.2 Incremental Similarity Search 25 7 Policies for Avoiding Distance Computations 26 7.1 Explanatory Example 27 7.2 Object-Pivot Distance Constraint 28 7.3 Range-Pivot Distance Constraint 30 7.4 Pivot-Pivot Distance Constraint 31 7.5 Double-Pivot Distance Constraint 33 7.6 Pivot Filtering 34 8 Metric Space Transformations 35 8.1 Metric Hierarchies 36 8.1.1 Lower-Bounding Functions 36 8.2 User-Defined Metric Functions 38 8.2.1 Searching Using Lower-Bounding Functions 38 8.3 Embedding Metric Space 39 8.3.1 Embedding Examples 39 8.3.2 Reducing Dimensionality 40 9 Approximate Similarity Search 41 9.1 Principles 41 9.2 Generic Algorithms 44 9.3 Measures of Performance 46 9.3.1 Improvement in Efficiency 46 9.3.2 Precision and Recall 46 9.3.3 Relative Error on Distances 48 9.3.4 Position Error 49 10 Advanced Issues 50 10.1 Statistics on Metric Datasets 51 10.1.1 Distribution and Density Functions 51 10.1.2 Distance Distribution and Density 52 10.1.3 Homogeneity of Viewpoints 54 10.2 Proximity of Ball Regions 55 10.3 Performance Prediction 58 Contents ix 10.4 Tree Quality Measures 60 10.5 Choosing Reference Points 63 2. SURVEY OF EXISTING APPROACHES 67 1 Ball Partitioning Methods 67 1.1 Burkhard-Keller Tree 6

kibana sample测试数据，sample中所有的数据分析来源于此。