人工智能与元启发式组合优化 A.I. and Combinatorial Optimization with Meta-Heuristics

图形算法、遗传算法、模拟退火、群体智能、启发式和元启发式

你会学到什么
理解为什么人工智能很重要
理解寻路算法(BFS、DFS和A*搜索)
理解启发式和元启发式
理解遗传算法
理解粒子群优化
理解模拟退火

流派:电子学习| MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥CG资源站 机译）|大小解压后:3.75 GB |时长:13h 6m



描述
本课程是关于人工智能和Python元启发式的基本概念。这个话题现在变得非常热门，因为这些学习算法可以用于从软件工程到投资银行的多个领域。例如，学习算法可以识别有助于检测癌症的模式。我们可以构建算法，对市场上的股价走势有很好的猜测。

###寻路算法###

第1部分-广度优先搜索(BFS)

什么是广度优先搜索算法

为什么在人工智能中使用图算法

第2部分-深度优先搜索

什么是深度优先搜索算法

用迭代和递归实现

深度优先搜索堆栈内存可视化

迷宫逃生应用

第3节- A*搜索算法

什么是A*搜索算法

Dijkstra算法和A*搜索有什么区别

什么是启发式

曼哈顿距离和欧几里德距离

### META-HEURISTICS ###



第4节-模拟退火

什么是模拟退火

如何求函数的极值

如何解决组合优化问题

旅行推销员问题

用模拟退火法求解数独问题

第5节-遗传算法

什么是遗传算法

人工进化和自然选择

交叉和变异

求解背包问题和N皇后问题

第6节-粒子群优化算法

什么是群体智能

什么是粒子群优化算法

### PYTHON编程速成班###

Python编程基础

基本数据结构

内存管理基础

面向对象程序设计

NumPy

在第一章中，我们将讨论基本的图算法——广度优先搜索(BFS)、深度优先搜索(DFS)和A*搜索算法。几个高级算法可以借助图来解决，所以在我看来这些算法是至关重要的。

接下来的章节是关于启发式和元启发式。我们将考虑模拟退火、遗传算法和粒子群优化的理论和实现，包括几个问题，如著名的N皇后问题、旅行商问题等。

感谢您参加课程，让我们开始吧！

这门课是给谁的
初学Python的程序员对人工智能和组合优化很好奇



Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.61 GB | Duration: 13h 6m

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Heuristics and Meta-Heuristics

What you’ll learn
understand why artificial intelligence is important
understand pathfinding algorithms (BFS, DFS and A* search)
understand heuristics and meta-heuristics
understand genetic algorithms
understand particle swarm optimization
understand simulated annealing

Description
This course is about the fundamental concepts of artificial intelligence and meta-heuristics with Python. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market.

### PATHFINDING ALGORITHMS ###

Section 1 – Breadth-First Search (BFS)

what is breadth-first search algorithm

why to use graph algorithms in AI

Section 2 – Depth-First Search (DFS)

what is depth-first search algorithm

implementation with iteration and with recursion

depth-first search stack memory visualization

maze escape application

Section 3 – A* Search Algorithm

what is A* search algorithm

what is the difference between Dijkstra’s algorithm and A* search

what is a heuristic

Manhattan distance and Euclidean distance

### META-HEURISTICS ###

Section 4 – Simulated Annealing

what is simulated annealing

how to find the extremum of functions

how to solve combinatorial optimization problems

travelling salesman problem (TSP)

solving the Sudoku problem with simulated annealing

Section 5 – Genetic Algorithms

what are genetic algorithms

artificial evolution and natural selection

crossover and mutation

solving the knapsack problem and N queens problem

Section 6 – Particle Swarm Optimization (PSO)

what is swarm intelligence

what is the Particle Swarm Optimization algorithm

### PYTHON PROGRAMMING CRASH COURSE ###

Python programming fundamentals

basic data structures

fundamentals of memory management

object oriented programming (OOP)

NumPy

In the first chapters we are going to talk about the fundamental graph algorithms – breadth-first search (BFS), depth-first search (DFS) and A* search algorithms. Several advanced algorithms can be solved with the help of graphs, so in my opinion these algorithms are crucial.

The next chapters are about heuristics and meta-heuristics. We will consider the theory as well as the implementation of simulated annealing, genetic algorithms and particle swarm optimization – with several problems such as the famous N queens problem, travelling salesman problem (TSP) etc.

Thanks for joining the course, let’s get started!

Who this course is for
Beginner Python programmers curious about artificial intelligence and combinatorial optimization
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