# Machine-Learning-Navigation **Repository Path**: XGX_CURRY_TOM/Machine-Learning-Navigation ## Basic Information - **Project Name**: Machine-Learning-Navigation - **Description**: No description available - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2021-12-28 - **Last Updated**: 2025-02-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README 欢迎访问[OmegaXYZ.com](https://www.omegaxyz.com) Last Update: 2020-08-15 NLP, Knowledge Graph Added 原始网页：https://www.omegaxyz.com/archive/machine-learning-navigator/ # **机器学习基本理论** ## **基本术语与方法** [**机器学习术语表**](http://www.omegaxyz.com/2018/03/22/machine_learning_glossary/) **[机器学习数据集的基本概念](http://www.omegaxyz.com/2018/08/24/ml-datasets/)** **[机器学习领域权威会议与期刊整理](http://www.omegaxyz.com/2018/01/15/machine_learning_co/)** **[计算机领域顶级会议、期刊、人物与国家排名2019](http://www.omegaxyz.com/2019/03/01/guide2research/)** **[机器学习分类与回归](http://www.omegaxyz.com/2018/07/04/classification-and-regression/)** **[NFL（没有免费的午餐）定理](http://www.omegaxyz.com/2018/05/22/nfl/)** **[机器学习中一些模型为什么要对数据归一化？](http://www.omegaxyz.com/2018/01/12/machine_learning_guiyihua/)** **[机器学习数据集划分及交叉验证的选择](http://www.omegaxyz.com/2018/04/12/divide_data_cross_validation/)** **[深度学习和经典机器学习对比](http://www.omegaxyz.com/2018/04/09/deeplandml/)** **[机器学习相似性度量（距离度量）](http://www.omegaxyz.com/2018/04/16/similarity_measure/)** **[收敛性变量与多样性变量的区别](http://www.omegaxyz.com/2018/03/10/convergence_diverdity_variables/)** **[机器学习非平衡数据集概述](http://www.omegaxyz.com/2017/08/28/disequilibrium_data_set/)** **[梯度下降法](http://www.omegaxyz.com/2019/04/19/gradient-descent/)** **[ROC曲线/AUC曲线与混淆矩阵介绍](http://www.omegaxyz.com/2017/08/27/rocandauc/)** **[混淆矩阵简介与Python实现](http://www.omegaxyz.com/2018/05/25/python_confusion/)** **[高斯变异](http://www.omegaxyz.com/2018/03/13/gaussian_mutation/)** **[监督学习与无监督学习](http://www.omegaxyz.com/2018/02/05/supervised_learning/)** **[朴素贝叶斯分类器](http://www.omegaxyz.com/2017/11/22/bayes/)** **[互信息公式及概述](http://www.omegaxyz.com/2018/08/02/mi/)** **[最大互信息系数(MIC)详解](http://www.omegaxyz.com/2018/01/18/mic/)** **[MIC（最大互信息系数）的计算](http://www.omegaxyz.com/2018/02/21/minepy/)** **[遗传算法的交叉变异详解](http://www.omegaxyz.com/2018/04/21/crossover_mutation/)** **[One-Hot编码详解与Python实现](http://www.omegaxyz.com/2018/04/22/one-hot/)** **[信息熵（香农熵）概述](http://www.omegaxyz.com/2018/05/07/information_entropy/)** **[机器学习多分类问题转二分类问题](http://www.omegaxyz.com/2018/05/20/multi2bi_ml/)** **[特征工程------特征离散化与选择](http://www.omegaxyz.com/2018/07/23/feature-discretization-selection/)** **[特征离散化概述](http://www.omegaxyz.com/2018/07/18/feature-discretisation/)** **[列向量互信息计算通用MATLAB代码](http://www.omegaxyz.com/2018/08/10/vector-mi-matlab/)** **[高维（多变量）优化问题的技术与瓶颈](http://www.omegaxyz.com/2018/09/10/lsgo/)** **[对极大似然估计的理解](http://www.omegaxyz.com/2019/03/31/maximum-likelihood-estimation/)** **[Wilcoxon秩和检验简介与MATLAB实现](http://www.omegaxyz.com/2019/04/07/ranksum/)** **[批量学习与在线学习概述](http://www.omegaxyz.com/2018/05/02/batch_and_online_learning/)** **[mat格式数据集转换为arff与txt格式](https://www.omegaxyz.com/2019/08/07/mat2arffortxt/)** **[机器学习数据集制作与划分MATLAB实现](https://www.omegaxyz.com/2019/06/26/divide-dataset/)** **[基于留一法的快速KNN代码](https://www.omegaxyz.com/2020/06/17/rapid-knn/)** ## **特征选择概述** **[特征选择的基本方法概述](http://www.omegaxyz.com/2017/09/25/feature_section_measures_intro/)** **[特征选择与特征提取（降维）](http://www.omegaxyz.com/2018/01/18/reduce_dimensions/)** **[特征选择方法与理解](http://www.omegaxyz.com/2018/01/19/fs_understanding/)** ## **多目标优化问题** **[多目标优化问题详解](http://www.omegaxyz.com/2017/09/02/mop/)** **[Pareto最优解几点解释](http://www.omegaxyz.com/2017/04/16/theexpofpareto/)** **[多目标优化问题概述](http://www.omegaxyz.com/2017/04/13/briefintroductionofpareto/)** **[多目标进化算法的一般流程](http://www.omegaxyz.com/2017/04/16/theprocedureofmultioa/)** **[非支配排序算法通用MATLAB代码](http://www.omegaxyz.com/2018/02/11/ndsort/)** **[反世代距离评价指标IGD](http://www.omegaxyz.com/2019/01/14/igd/)** **[HyperVolume多目标评价指标概述](http://www.omegaxyz.com/2019/03/09/hypervolume/)** ## **分类算法** **[决策树概述](http://www.omegaxyz.com/2018/02/10/decision_tree/)** **[朴素贝叶斯分类器](http://www.omegaxyz.com/2017/11/22/bayes/)** [**KNN****算法概述**](http://www.omegaxyz.com/2018/01/08/knn/) **[KNN算法MATLAB实现](http://www.omegaxyz.com/2018/03/24/knn_matlab/)** **[MATLAB中SVM（支持向量机）的用法](http://www.omegaxyz.com/2018/01/23/matlab_svm/)** **[Python支持向量机（SVM）实例](http://www.omegaxyz.com/2018/01/12/python_svm/)** **[AdaBoost简介及Python应用](http://www.omegaxyz.com/2019/04/11/adaboost-python/)** ## **聚类算法** **[k-means算法概述](http://www.omegaxyz.com/2018/01/27/kmeans/)** **[MATLAB工具箱k-means算法](http://www.omegaxyz.com/2018/01/28/kmeans_matlab/)** **[基于训练集的代理模型生成算法（DROP3、AGG）](http://www.omegaxyz.com/2018/05/08/drop3_agg/)** **[Python利用sklearn进行kmeans聚类](http://www.omegaxyz.com/2018/08/28/python%e5%88%a9%e7%94%a8sklearn%e8%bf%9b%e8%a1%8ckmeans%e8%81%9a%e7%b1%bb/)** **[kmeans聚类选择最优K值python实现](http://www.omegaxyz.com/2018/09/03/k-means-find-k/)** **[DBSCAN聚类算法Python实现](http://www.omegaxyz.com/2019/03/21/dbscan-python/)** --- # **知识图谱与社交网络** 知识图谱（Knowledge Graph），在图书情报界称为知识域可视化或知识领域映射地图，是显示知识发展进程与结构关系的一系列各种不同的图形，用可视化技术描述知识资源及其载体，挖掘、分析、构建、绘制和显示知识及它们之间的相互联系。知识图谱是通过将应用数学、图形学、信息可视化技术、信息科学等学科的理论与方法与计量学引文分析、共现分析等方法结合，并利用可视化的图谱形象地展示学科的核心结构、发展历史、前沿领域以及整体知识架构达到多学科融合目的的现代理论。它能为学科研究提供切实的、有价值的参考。 **[Neo4j数据导入与可视化](https://www.omegaxyz.com/2020/01/17/neo4j_introduction/)** **[知识图谱嵌入的应用场景](https://www.omegaxyz.com/2020/01/15/kge-application/)** **[融合事实信息的知识图谱嵌入——语义匹配模型](https://www.omegaxyz.com/2020/01/13/kge-semantic-matching-models/)** **[融合事实信息的知识图谱嵌入——平移距离模型](https://www.omegaxyz.com/2020/01/12/kge-translational-distance-models/)** **[Python社交网络——NetworkX入门](https://www.omegaxyz.com/2020/01/07/python-networkx/)** **[可视化图布局算法简介](https://www.omegaxyz.com/2020/02/01/graph-layout/)** **[用户身份链接方法——DeepLink](https://www.omegaxyz.com/2020/01/27/deeplink/)** **[图注意力网络(GAT) TensorFlow实现](https://www.omegaxyz.com/2020/03/29/gat-tensorflow/)** **[知识图谱可视化Demo](https://www.omegaxyz.com/2020/03/03/knowledge-graph-demo/)** **[知识融合（实体对齐）笔记](https://www.omegaxyz.com/2020/04/06/knowledge-fusion/)** **[知识图谱属性与关系区别](https://www.omegaxyz.com/2020/04/03/attribute-relation-embedding/)** **[知识图谱综述论文阅读](https://www.omegaxyz.com/2020/05/19/kg-survey/)** **[BERT-BiLSTM-CRF命名实体识别应用](https://www.omegaxyz.com/2020/05/18/bert-bilstm-crf/)** **[黑暗森林：知识图谱的前世今生](https://www.omegaxyz.com/2020/04/29/dark-forest-kg/)** **[基于知识图谱的问答系统Demo](https://www.omegaxyz.com/2020/04/28/kbqa-demo/)** **[Aminer学术社交网络数据知识图谱构建（三元组与嵌入）](https://www.omegaxyz.com/2020/07/12/aminer-academic-social-network/)** **[学科领域本体关系数据与可视化](https://www.omegaxyz.com/2020/07/13/domain_data_visualization/)** **[Dedupe去重与实体对齐](https://www.omegaxyz.com/2020/07/24/dedupe/)** **[Linux知识图谱](https://www.omegaxyz.com/2020/08/04/linux-knowledge-graph/)** --- # **Python机器学习** python机器学习只适合做小规模的算法和简单神经网络，适合入门新手学习，由于Python的效率极低，一般前沿研究中不使用Python进行大数据学习。因此下面很多算法是基于Python模块编写的机器学习代码。 **[Python支持向量机（SVM）实例](http://www.omegaxyz.com/2018/01/12/python_svm/)** **[Python机器学习房价预测 (斯坦福大学机器学习课程)](http://www.omegaxyz.com/2017/10/14/python_machinelearning_houseprices/)** **[Python粒子群优化算法实现（PSO）](http://www.omegaxyz.com/2018/01/12/python_pso/)** **[NLTK在python中对文字所表达的情感预测](http://www.omegaxyz.com/2017/12/15/nltk_emotion/)** **[Python机器学习与拼写检查器](http://www.omegaxyz.com/2017/12/26/python_check_word/)** **[Python英文搜索引擎（模糊搜索）](http://www.omegaxyz.com/2018/01/09/python_search_engine/)** **[基于词典的社交媒体内容的情感分析（Python实现）](http://www.omegaxyz.com/2018/03/02/weibo_emotion/)** **[One-Hot编码详解与Python实现](http://www.omegaxyz.com/2018/04/22/one-hot/)** **[英文字母信息熵与冗余度计算Python实现](http://www.omegaxyz.com/2018/05/09/information_entropy_alpha/)** **[混淆矩阵简介与Python实现](http://www.omegaxyz.com/2018/05/25/python_confusion/)** **[Python利用sklearn进行kmeans聚类](http://www.omegaxyz.com/2018/08/28/python%e5%88%a9%e7%94%a8sklearn%e8%bf%9b%e8%a1%8ckmeans%e8%81%9a%e7%b1%bb/)** **[kmeans聚类选择最优K值python实现](http://www.omegaxyz.com/2018/09/03/k-means-find-k/)** **[Astar算法解决八数码问题Python实现（GUI）](http://www.omegaxyz.com/2019/01/22/eight-puzzle-python/)** **[DBSCAN聚类算法Python实现](http://www.omegaxyz.com/2019/03/21/dbscan-python/)** **[AdaBoost简介及Python应用](http://www.omegaxyz.com/2019/04/11/adaboost-python/)** **[LLE（Locally Linear Embedding）算法](https://www.omegaxyz.com/2020/01/21/lle/)** **[基于LDA的文本主题聚类Python实现](https://www.omegaxyz.com/2020/02/24/lda-topic/)** --- # **深度学习与神经网络** 深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示。 **[深度学习和经典机器学习对比](http://www.omegaxyz.com/2018/04/09/deeplandml/)** **[神经网络模型详解](http://www.omegaxyz.com/2018/04/13/ann_intro/)** **[神经网络感知机概述](http://www.omegaxyz.com/2018/05/01/perceptron/)** **[BP神经网络MATLAB实现](http://www.omegaxyz.com/2018/07/03/bp-matlab/)** **[卷积神经网络CNN概述上（黑白图像卷积）](http://www.omegaxyz.com/2018/06/02/cnn1/)** **[卷积神经网络GoogLeNet概述](http://www.omegaxyz.com/2018/07/14/googlenet/)** **[Tensor（张量）的简介与运用](http://www.omegaxyz.com/2018/05/27/whatstensor/)** **[TensorFlow简单卷积神经（CNN）网络实现](http://www.omegaxyz.com/2018/06/04/tensorflow-simple-cnn/)** **[TensorFlow实现简单神经网络分类问题](http://www.omegaxyz.com/2018/05/31/tensorflow-simple-classification/)** **[TensorFlow双层神经网络函数拟合实例（基于Python）](http://www.omegaxyz.com/2018/04/19/2nn_tensorflow_py/)** **[TensorFlow基本语句与概念](http://www.omegaxyz.com/2018/04/20/tensorflow_intro/)** **[TensorFlow手写识别入门](http://www.omegaxyz.com/2018/04/26/tensorflow_mnist_simple/)** **[TensorBoard显示TensorFlow流程图](http://www.omegaxyz.com/2018/04/27/tensorboard_tensorflow/)** **[神经网络（TensorFlow）游乐场](http://www.omegaxyz.com/2018/07/19/tensorflow-playground/)** **[卷积神经网络CNN概述下（RGB图像卷积与单层神经网络）](http://www.omegaxyz.com/2018/08/14/cnn2-rgb/)** **[OpenCV人脸识别-静态图片与动态摄像头](http://www.omegaxyz.com/2018/08/20/opencv-camera-pic/)** **[NLP之word2vec简介](http://www.omegaxyz.com/2018/11/13/word2vec/)** **[基于移动设备与CNN的眼动追踪技术简介](http://www.omegaxyz.com/2018/12/20/eye-tracking-for-everyone/)** **[基于眼动跟踪技术的人机交互方法研究](http://www.omegaxyz.com/2019/01/18/hci-eye-tracking/)** **[王者荣耀AI论文：层次宏观策略模型](http://www.omegaxyz.com/2018/12/25/moba-ai/)** **[PyTorch入门](http://www.omegaxyz.com/2019/01/12/pytorch-introduction/)** **[图注意力网络(GAT) TensorFlow实现](https://www.omegaxyz.com/2020/03/29/gat-tensorflow/)** **[个人主页信息提取器](https://www.omegaxyz.com/2020/06/18/fast-profile-extractor/)** --- # **特征选择算法** 特征选择( Feature Selection )也称特征子集选择( Feature Subset Selection , FSS )，或属性选择( Attribute Selection )。是指从已有的M个特征(Feature)中选择N个特征使得系统的特定指标最优化，是从原始特征中选择出一些最有效特征以降低数据集维度的过程,是提高学习算法性能的一个重要手段,也是模式识别中关键的数据预处理步骤。对于一个学习算法来说,好的学习样本是训练模型的关键。 **[机器学习如何做特征选择实验](http://www.omegaxyz.com/2018/04/18/how_to_do_fs_experiment/)** **[特征选择方法与理解](http://www.omegaxyz.com/2018/01/19/fs_understanding/)** **[特征选择的基本方法概述](http://www.omegaxyz.com/2017/09/25/feature_section_measures_intro/)** **[特征选择与特征提取（降维）](http://www.omegaxyz.com/2018/01/18/reduce_dimensions/)** **[SFS与SBS特征选择算法](http://www.omegaxyz.com/2018/04/03/sfsandsbs/)** **[基于MIC（最大互信息系数）的特征选择](http://www.omegaxyz.com/2018/02/22/micfs/)** **[NSGA2算法特征选择MATLAB实现（多目标）](http://www.omegaxyz.com/2018/01/23/nsga2_fs/)** **[PSO算法特征选择MATLAB实现（单目标）](http://www.omegaxyz.com/2018/01/21/psofs/)** **[基于训练集动态代理模型的PSO特征选择算法](http://www.omegaxyz.com/2018/05/10/ts_surrogate_pso_fs/)** **[基于VNS及马尔科夫毯分组的高维特征选择算法](http://www.omegaxyz.com/2017/11/29/pgvns/)** **[基于互信息的特征选择算法MATLAB实现](http://www.omegaxyz.com/2018/08/03/mifs/)** **[特征选择Filter方法详解](http://www.omegaxyz.com/2018/04/23/fs_filter/)** **[在线特征选择概述](http://www.omegaxyz.com/2018/05/03/online_feature_selection/)** **[特征离散化与选择EPSO算法详解](http://www.omegaxyz.com/2018/08/08/fdfs-epso/)** **[基于PSO的特征离散化与选择算法](http://www.omegaxyz.com/2018/07/27/pso-fdfs/)** **[基于非支配排序的多目标PSO算法](http://www.omegaxyz.com/2018/09/01/nspso/)** **[基于变长PSO的高维特征选择算法（VLPSO）概述](http://www.omegaxyz.com/2018/09/16/vlpso/)** **[基于非支配排序的多目标PSO算法MATLAB实现](http://www.omegaxyz.com/2018/09/22/nspso_matlab/)** **[基于拥挤距离与变异支配的多目标PSO算法](http://www.omegaxyz.com/2018/10/15/cmdpso/)** **[最大相关最小冗余（mRMR）算法](http://www.omegaxyz.com/2018/10/27/mrmrfs/)** **[Kaggle入门泰坦尼克号乘客生还预测](http://www.omegaxyz.com/2019/03/10/kaggle-titanic/)** **[基于稀疏大规模矩阵的多目标进化算法简介](http://www.omegaxyz.com/2019/05/12/sparse-ea/)** --- # **自然语言处理（NLP）** **[NLP之word2vec简介](http://www.omegaxyz.com/2018/11/13/word2vec/)** **[基于WMD（词移距离）的句子相似度分析简介](http://www.omegaxyz.com/2018/11/22/wmd/)** **[基于有监督的词移距离（SWMD）简介](http://www.omegaxyz.com/2018/11/27/swmd/)** **[基于WMD（词移距离）的句子相似度分析MATLAB代码](http://www.omegaxyz.com/2018/12/09/wmd-matlab/)** **[基于LDA的文本主题聚类Python实现](https://www.omegaxyz.com/2020/02/24/lda-topic/)** **[BERT-BiLSTM-CRF命名实体识别应用](https://www.omegaxyz.com/2020/05/18/bert-bilstm-crf/)** **[个人主页信息提取器](https://www.omegaxyz.com/2020/06/18/fast-profile-extractor/)** --- # **演化计算算法** 演化计算是模拟自然界中的生物的演化过程产生的一种群体导向的随机搜索技术和方法。 ## **基本算法** **[进化（演化）计算概述](http://www.omegaxyz.com/2019/01/20/evolutionary-computing/)** **[基于粒子交互学习策略的PSO算法（IIL-PSO）](http://www.omegaxyz.com/2018/01/16/iil_pso/)** **[粒子群优化（PSO）算法概述](http://www.omegaxyz.com/2017/05/04/introductionofpso/)** **[Python粒子群优化算法实现（PSO）](http://www.omegaxyz.com/2018/01/12/python_pso/)** **[基于柯西和高斯分布的单目标PSO](http://www.omegaxyz.com/2018/01/09/cauchy_and_gaussian-based_pso/)** **[基于三重竞争机制的PSO算法（CSO）](http://www.omegaxyz.com/2018/05/19/tcso/)** **[多目标CSO算法（MOCSO）理解](http://www.omegaxyz.com/2017/06/24/mocso1/)** **[基于训练集动态代理模型的PSO特征选择算法](http://www.omegaxyz.com/2018/05/10/ts_surrogate_pso_fs/)** **[基于非支配排序的多目标PSO算法](http://www.omegaxyz.com/2018/09/01/nspso/)** **[演化计算基本方法与思想](http://www.omegaxyz.com/2018/03/21/ec_intro/)** **[基于量子遗传的函数寻优算法MATLAB实现](http://www.omegaxyz.com/2018/04/02/quantum/)** **[人工鱼群算法MATLAB实现](http://www.omegaxyz.com/2018/04/04/af/)** **[蚁群算法（ACO）最短路径规划（MATLAB）](http://www.omegaxyz.com/2018/01/27/aco_routes/)** **[蚁群算法（ACO）MATLAB实现](http://www.omegaxyz.com/2018/01/26/aco/)** **[蚁群算法（ACO）旅行商问题（TSP）路径规划MATLAB实现](http://www.omegaxyz.com/2018/07/10/aco-tsp/)** **[蚁群算法最短路径规划多出口情况及问题答疑](http://www.omegaxyz.com/2019/01/28/aco_routes2/)** **[基于非支配排序的多目标PSO算法MATLAB实现](http://www.omegaxyz.com/2018/09/22/nspso_matlab/)** **[基于拥挤距离与变异支配的多目标PSO算法](http://www.omegaxyz.com/2018/10/15/cmdpso/)** **[进化计算中基于分类的预处理代理模型](http://www.omegaxyz.com/2018/11/24/cps/)** **[遗传算法解决TSP问题MATLAB实现（详细）](http://www.omegaxyz.com/2019/01/21/matlab-tsp-all/)** **[基于进化计算的NP难题求解的研究综述](http://www.omegaxyz.com/2019/01/27/ea-np/)** **[基于迭代局部搜索和随机惯性权重的BA算法MATLAB实现（ILSSIWBA）](http://www.omegaxyz.com/2019/02/28/ilssiwba/)** ## **NSGA2****算法** **[NSGA-II多目标遗传算法概述](http://www.omegaxyz.com/2017/04/14/nsga-iiintro/)** **[NSGA2算法MATLAB实现（能够自定义优化函数）](http://www.omegaxyz.com/2018/01/22/new_nsga2/)** **[NSGA2算法特征选择MATLAB实现（多目标）](http://www.omegaxyz.com/2018/01/23/nsga2_fs/)** **[NSGA-Ⅱ算法Matlab实现（测试函数为ZDT1）](http://www.omegaxyz.com/2017/05/04/nsga2matlabzdt1/)** **[NSGA-Ⅱ算法C++实现（测试函数为ZDT1）](http://www.omegaxyz.com/2017/04/20/nsga2zdt1/)** **[NSGA-II快速非支配排序算法理解](http://www.omegaxyz.com/2017/04/19/nsga2fastsort/)** **[随机固定分组合作协同进化NSGA2算法（CCNSGA2）](http://www.omegaxyz.com/2018/02/08/ccnsga2/)** **[遗传算法的交叉变异详解](http://www.omegaxyz.com/2018/04/21/crossover_mutation/)** **[遗传算法解决TSP问题MATLAB实现（详细）](http://www.omegaxyz.com/2019/01/21/matlab-tsp-all/)** **[MATLAB遗传算法工具箱简介](http://www.omegaxyz.com/2018/07/13/matlab-ga-tool/)** ## **粒子群优化算法（****PSO****）** **[MATLAB粒子群优化算法实现（PSO）](http://www.omegaxyz.com/2018/01/17/matlab_pso/)** **[粒子群优化（PSO）算法概述](http://www.omegaxyz.com/2017/05/04/introductionofpso/)** **[Python粒子群优化算法实现（PSO）](http://www.omegaxyz.com/2018/01/12/python_pso/)** **[随机固定分组合作协同进化PSO算法（CCPSO）](http://www.omegaxyz.com/2018/01/29/ccpso_std/)** **[PSO算法特征选择MATLAB实现（单目标）](http://www.omegaxyz.com/2018/01/21/psofs/)** **[基于柯西和高斯分布的单目标PSO](http://www.omegaxyz.com/2018/01/09/cauchy_and_gaussian-based_pso/)** **[PSO算法的改进策略](http://www.omegaxyz.com/2018/07/09/improved-simple-pso/)** ## **蚁群算法（****ACO****）** **[蚁群算法（ACO）最短路径规划（MATLAB）](http://www.omegaxyz.com/2018/01/27/aco_routes/)** **[蚁群算法（ACO）MATLAB实现](http://www.omegaxyz.com/2018/01/26/aco/)** **[蚁群算法（ACO）旅行商问题（TSP）路径规划MATLAB实现](http://www.omegaxyz.com/2018/07/10/aco-tsp/)** **[蚁群算法最短路径规划多出口情况及问题答疑](http://www.omegaxyz.com/2019/01/28/aco_routes2/)** ## **蝙蝠算法** **[经典蝙蝠算法MATLAB实现](http://www.omegaxyz.com/2019/02/12/ba-matlab/)** **[基于迭代局部搜索和随机惯性权重的BA算法MATLAB实现（ILSSIWBA）](http://www.omegaxyz.com/2019/02/28/ilssiwba/)** ## **其它算法** **[模拟退火算法（SAA）C语言与MATLAB实现](http://www.omegaxyz.com/2018/01/25/saa/)** **[变邻域搜索算法（Variable Neighborhood Search,VNS）](http://www.omegaxyz.com/2017/11/27/vns/)** **[基于稀疏大规模矩阵的多目标进化算法简介](http://www.omegaxyz.com/2019/05/12/sparse-ea/)** ## **协同进化/演化算法** 协同演化算法(coevolutionary algorithms,CEA)是当前国际上计算智能研究的一个热点,它运用生物协同演化的思想,是针对演化算法的不足而兴起的,通过构造两个或多个种群,建立它们之间的竞争或合作关系,多个种群通过相互作用来提高各自性能,适应复杂系统的动态演化环境,以达到种群优化的目的。 **[合作协同进化算法概述（Cooperative Coevolution）](http://www.omegaxyz.com/2017/10/14/cooperative_coevolution/)** **[广义协同进化算法概述](http://www.omegaxyz.com/2018/03/20/coevolution/)** **[合作协同进化详解及伪代码](http://www.omegaxyz.com/2018/03/06/cc_details/)** **[随机固定分组合作协同进化NSGA2算法（CCNSGA2）](http://www.omegaxyz.com/2018/02/08/ccnsga2/)** **[随机固定分组合作协同进化PSO算法（CCPSO）](http://www.omegaxyz.com/2018/01/29/ccpso_std/)** **[差分分组合作协同进化MATLAB代码](http://www.omegaxyz.com/2017/10/25/cooperative_coevolution_matlab/)** **[差分分组的合作协同进化的大规模优化算法概述](http://www.omegaxyz.com/2017/09/29/ccdifferential_grouping/)** **[差分分组的合作协同进化的大规模优化算法详解](http://www.omegaxyz.com/2018/06/05/ccdifferential_grouping_new/)** **[多目标协同进化算法概要](http://www.omegaxyz.com/2018/06/13/cmoea/)** ## **前沿演化算法** 本部分展现的论文中讨论和提出的演化学习算法。 ### **NSGA2算法** **[NSGA-II多目标遗传算法概述](http://www.omegaxyz.com/2017/04/14/nsga-iiintro/)** **[NSGA2算法MATLAB实现（能够自定义优化函数）](http://www.omegaxyz.com/2018/01/22/new_nsga2/)** **[NSGA2算法特征选择MATLAB实现（多目标）](http://www.omegaxyz.com/2018/01/23/nsga2_fs/)** **[NSGA-Ⅱ算法Matlab实现（测试函数为ZDT1）](http://www.omegaxyz.com/2017/05/04/nsga2matlabzdt1/)** **[随机固定分组合作协同进化NSGA2算法（CCNSGA2）](http://www.omegaxyz.com/2018/02/08/ccnsga2/)** **[NSGA-Ⅱ算法C++实现（测试函数为ZDT1）](http://www.omegaxyz.com/2017/04/20/nsga2zdt1/)** **[遗传算法的交叉变异详解](http://www.omegaxyz.com/2018/04/21/crossover_mutation/)** ### **前沿PSO算法** **[基于粒子交互学习策略的PSO算法（IIL-PSO）](http://www.omegaxyz.com/2018/01/16/iil_pso/)** **[基于柯西和高斯分布的单目标PSO](http://www.omegaxyz.com/2018/01/09/cauchy_and_gaussian-based_pso/)** **[多目标CSO算法（MOCSO）理解](http://www.omegaxyz.com/2017/06/24/mocso1/)** **[基于三重竞争机制的PSO算法（CSO）](http://www.omegaxyz.com/2018/05/19/tcso/)** **[基于训练集动态代理模型的PSO特征选择算法](http://www.omegaxyz.com/2018/05/10/ts_surrogate_pso_fs/)** **[PSO算法的改进策略](http://www.omegaxyz.com/2018/07/09/improved-simple-pso/)** **[特征离散化与选择EPSO算法详解](http://www.omegaxyz.com/2018/08/08/fdfs-epso/)** **[基于变长PSO的高维特征选择算法（VLPSO）概述](http://www.omegaxyz.com/2018/09/16/vlpso/)** **[基于PSO的特征离散化与选择算法](http://www.omegaxyz.com/2018/07/27/pso-fdfs/)** **[基于非支配排序的多目标PSO算法MATLAB实现](http://www.omegaxyz.com/2018/09/22/nspso_matlab/)** **[基于拥挤距离与变异支配的多目标PSO算法](http://www.omegaxyz.com/2018/10/15/cmdpso/)** ### **差分分组（进化）算法** **[差分分组合作协同进化MATLAB代码](http://www.omegaxyz.com/2017/10/25/cooperative_coevolution_matlab/)** **[差分分组的合作协同进化的大规模优化算法概述](http://www.omegaxyz.com/2017/09/29/ccdifferential_grouping/)** **[差分分组的合作协同进化的大规模优化算法详解](http://www.omegaxyz.com/2018/06/05/ccdifferential_grouping_new/)** **[一个更快更准确的差分分组大规模黑盒子优化算法概述](http://www.omegaxyz.com/2017/10/17/dg2/)** **[差分进化算法 (Differential Evolution)概述](http://www.omegaxyz.com/2018/04/24/differential_evolution_intro/)** **[差分进化算法Python实现](http://www.omegaxyz.com/2018/04/25/de_python/)** ### **其它算法** [**超启发式算法**](http://www.omegaxyz.com/2018/03/11/hyper-heuristic_algorithm/) **[马尔科夫毯（Markov Blankets）](http://www.omegaxyz.com/2017/11/27/markov-blanket/)** **[变邻域搜索算法（Variable Neighborhood Search,VNS）](http://www.omegaxyz.com/2017/11/27/vns/)** **[基于VNS及马尔科夫毯分组的高维特征选择算法](http://www.omegaxyz.com/2017/11/29/pgvns/)** **[自我评价算法（SEE）框架](http://www.omegaxyz.com/2018/03/16/see_simple/)** **[基于训练集的代理模型生成算法（DROP3、AGG）](http://www.omegaxyz.com/2018/05/08/drop3_agg/)** **[基于迭代局部搜索和随机惯性权重的BA算法MATLAB实现（ILSSIWBA）](http://www.omegaxyz.com/2019/02/28/ilssiwba/)** --- # **PyTorch** **[PyTorch入门](http://www.omegaxyz.com/2019/01/12/pytorch-introduction/)** --- # **Kaggle** **[Kaggle入门泰坦尼克号乘客生还预测](http://www.omegaxyz.com/2019/03/10/kaggle-titanic/)** --- # **其他** **[数据分析岗位面试必备](https://www.omegaxyz.com/2020/02/17/data-analysis-interview/)** **[推荐系统摘要](https://www.omegaxyz.com/2020/02/16/recommendation-system-abstract/)** **[FR算法（Fruchterman-Reingold）Python实现](https://www.omegaxyz.com/2020/04/12/fruchterman-reingold-python/)** ---