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所在平台: Udemy |
课程主页: https://www.udemy.com/course/genetic-algorithm-a-to-z-with-combinatorial-problems-d/
课程评论：没有评论
《遗传算法：从A到Z与组合优化问题》在线课程全面实用，旨在为解决现实世界优化问题提供一个直接的集成框架。作为元启发式算法领域的首门实践课程，它对学生、研究人员和从业者至关重要。 课程内容涵盖： * **遗传算法基础理论**：从基本概念开始。 * **遗传算法实现**： * **二元遗传算法 (Binary GA)**：在Matlab中实现。 * **实数遗传算法 (Real GA)**：进一步学习。 * **组合优化问题应用**： * **运输问题** * **集线器选址问题 (Hub Location Problem, HLP)** * **二次分配问题 (Quadratic Assignment Problem)** * **旅行商问题 (Traveling Salesman Problem, TSP)** * 教授如何用遗传算法解决这些问题，构建解决组合优化问题的通用框架。 * **遗传算法参数调优**： * **田口方法 (Taguchi Method)** * **响应面法 (Response Surface Methodology, RSM)** * **统计分析**：使用Minitab和Design Expert软件进行有效的元启发式算法比较。 **课程亮点**： * 解决各类现实世界挑战性问题。 * 学习管理现实问题中的惩罚函数。 * 进行全面的统计分析。 * 为不同问题定义染色体。 * 掌握算法参数的运用。 课程包含大量编码视频和真实案例研究，提供丰富的实践机会，学完后，您将能够熟练运用遗传算法在Matlab中解决各类运筹学问题，并将不同元启发式算法应用于各种问题。这不仅仅是理论学习，更是掌握遗传算法在现实挑战中应用的实践指南。
This course on Genetic Algorithms (GA) is one of the most practical and comprehensive courses available, designed to provide an integrated framework for solving real-world optimization problems in the most straightforward manner. It is the first of its kind to offer a hands-on approach in the domain of metaheuristic algorithms, making it essential for students, researchers, and practitioners.The course begins with an introduction to the basic theory of GA, followed by the implementation of the simplest version of GA, the Binary GA, into Matlab. It then progresses to the continuous version, the Real GA. The primary focus will be on the Genetic Algorithm, a highly regarded optimization algorithm in the literature. Subsequent sections will introduce well-known operation research problems such as transportation, hub location (HLP), quadratic assignment, and travelling salesman (TSP) problems, and demonstrate how to solve them using GA. This approach will equip you with a comprehensive framework to tackle any combinatorial optimization problems. Additionally, the course will cover two renowned methods for tuning GA's parameters: the Taguchi method and the Response Surface Methodology (RSM). Finally, we will provide a statistical analysis using Minitab software and Design Expert to compare different metaheuristics effectively.Key features of this course include:• Solving various challenging real-world problems• Managing penalty functions in real-world problems• Conducting comprehensive statistical analysis• Defining chromosomes for different problems• Handling algorithm parametersThe course includes a plethora of coding videos, providing ample opportunity to practice the theory covered in the lectures. It also features several real case studies, allowing you to learn the process of solving challenging problems using GA.Upon completing this course, you will be well-versed in implementing GA on a wide range of operation research problems in Matlab. Consequently, you will be equipped to apply different metaheuristic algorithms to solve various problems.This course is not just a theoretical journey; it is a practical guide to mastering the application of Genetic Algorithms to real-world challenges. Equip yourself with the knowledge and skills required to excel in the field of operations research by enrolling in this course today.
