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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-ultimate-beginners-guide-to-genetic-algorithms-in-python/
课程评论：没有评论
课程名称：《Python中的遗传算法终极初学者指南》 课程概述：遗传算法是人工智能的一个重要领域，能够解决复杂的现实问题。这类算法在商业场景中有多种实际应用，例如帮助学校解决教师排班的问题，或帮助电信公司设计新的光网络等。本课程将教你全面了解遗传算法的基本知识，尤其是从零开始实现遗传算法，而不是使用现成的库。 课程内容分为两部分： 第一部分，我们将从头开始实现一个遗传算法，以解决一个常见的产品运输问题。假设需要在卡车上装载一些产品，我们需要选择最有利可图的产品，并且考虑到卡车的空间限制。该遗传算法的目标是选择最佳的产品组合，以最大化公司的利润。课程最后，我们还将把算法与MySql数据库集成，以便更好地处理商业应用。 第二部分，在掌握基本概念和从头实现遗传算法之后，我们将学习如何利用库来解决相同的问题。除了产品运输案例外，我们还将解决关于寻找团体旅行的最低机票价格的问题，使用库DEAP（Python中的分布式进化算法）和MLROSe进行比较。通过比较我们从头实现的遗传算法与库的结果，学生可以加深理解。 完成本课程后，学员将具备解决自身问题或公司问题的理论基础和实践能力，并为进一步学习更高级的内容做好准备。课程使用Google Colab逐步实施代码，确保学员无需担心软件安装或配置问题。
Genetic algorithms are an important area of Artificial Intelligence responsible for solving complex real world problems. There are several practical applications of this type of algorithm, which can be applied to problem solving in everyday business situations. A classic example is solving the problem of teacher schedule in schools, in which there are different combinations of schedules and classes and the goal is to build the schedule dynamically according to the number of classes and the availability of each teacher. Other examples are: telecommunications companies can design new optical networks, carriers can better plan the delivery route for goods, investors can choose the best investments; among several others.In this course, you will learn everything you need to enter the world of genetic algorithms! What makes this course unique is that you will learn the basic intuition and especially, the step-by-step implementation without using pre-built libraries. In other words, we are going to implement genetic algorithms from scratch using Python. If you have never heard about this subject, at the end of the course you will have all the theoretical and practical basis to solve your own problems or the problems of the company you work for!In part 1, we are going to implement a genetic algorithm from scratch to solve a very common problem that is related to transportation of products. Let's suppose we need to load some products on the truck, but we need to select the most profitable products and also take into account that there is not enough space on the truck to load them all. So, the goal of the genetic algorithm will be to choose the best set of products to maximize the profit of the company. At the end we will integrate our algorithm with a database in MySql, so it will be easier to know how to deal with commercial applications!In part 2 (after you learn the whole intuition and implement genetic algorithms from scratch), it's time to learn how to work with libraries to solve the same problem. In addition to the case study of product transportation, we will also solve another problem that is related to finding the lowest prices of airline tickets for people traveling in group. We will solve both problems using two libraries: DEAP (Distributed Evolutionary Algorithms in Python) and MLROSe. The interesting is that we will be able to compare the results of the libraries with the results of our genetic algorithm implemented from scratch.This can be considered the first course on genetic algorithms, and after completing it, you can move on to more advanced materials. At the end you will have the practical background to develop some simple projects and take more advanced courses. During the lectures, the code will be implemented step by step using Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine.
