Python
Optimization with Metaheuristics in Python Best Courses
Table of Contents
Optimization with Metaheuristics in Python Best Courses
Learn Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies, and Learn to Handle Constraints
What you’ll learn
Optimization with Metaheuristics in Python Best Courses
- Learn the foundations of optimization
- Understand metaheuristics such as Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies
- Be able to code metaheuristics in Python
- Handle constraints though penalties
Requirements
- Basic knowledge in Operations Research and Optimization – (not a must, but helpful)
- Basic programming skills in Python – (not a must, but helpful)
Description
This course will guide you on what optimization is and what metaheuristics are.
You will learn why we use metaheuristics in optimization problems as sometimes when you have a complex problem you’d like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used.
- Simulated Annealing
- Genetic Algorithm
- Tabu Search
- Evolutionary Strategies
With no packages and no libraries, learn to code them from scratch!! You will also learn how to handle constraints using the penalty method.
Here’s the awesome part –> you do NOT need to know Python programming!
- This course will teach you how to optimize continuous and combinatorial problems using Python
- Basically, you can think of this as not only a course that teaches you 4 well-known metaheuristics but also Python programming!
Who this course is for:
- Anyone who wants to learn about metaheuristics
- Anyone who wants to learn the Genetic Algorithm
- Who wants to learn Simulated Annealing
- Anyone who wants to learn Tabu Search
- Anyone who wants to learn Evolutionary Strategies
- Who wants to code metaheuristics in Python
- Anyone who wants to learn how to handle constraints
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